Overview

Dataset statistics

Number of variables49
Number of observations19991
Missing cells53052
Missing cells (%)5.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.2 MiB
Average record size in memory378.0 B

Variable types

Numeric22
Categorical24
Boolean3

Alerts

id has a high cardinality: 19893 distinct valuesHigh cardinality
title has a high cardinality: 19342 distinct valuesHigh cardinality
thumbnail_id has a high cardinality: 19248 distinct valuesHigh cardinality
permalink has a high cardinality: 19877 distinct valuesHigh cardinality
category_id has a high cardinality: 725 distinct valuesHigh cardinality
domain_id has a high cardinality: 570 distinct valuesHigh cardinality
tags has a high cardinality: 1166 distinct valuesHigh cardinality
shipping__tags has a high cardinality: 119 distinct valuesHigh cardinality
seller__nickname has a high cardinality: 3478 distinct valuesHigh cardinality
seller__tags has a high cardinality: 175 distinct valuesHigh cardinality
variation_filters has a high cardinality: 214 distinct valuesHigh cardinality
order_backend is highly overall correlated with seller__car_dealer_logoHigh correlation
price is highly overall correlated with installments__amount and 1 other fieldsHigh correlation
sold_quantity is highly overall correlated with days_remaining and 1 other fieldsHigh correlation
available_quantity is highly overall correlated with seller__car_dealer_logoHigh correlation
seller__id is highly overall correlated with years_active and 1 other fieldsHigh correlation
seller__seller_reputation__transactions__canceled is highly overall correlated with seller__seller_reputation__transactions__completed and 6 other fieldsHigh correlation
seller__seller_reputation__transactions__completed is highly overall correlated with seller__seller_reputation__transactions__canceled and 6 other fieldsHigh correlation
seller__seller_reputation__transactions__ratings__negative is highly overall correlated with seller__seller_reputation__transactions__ratings__neutral and 3 other fieldsHigh correlation
seller__seller_reputation__transactions__ratings__neutral is highly overall correlated with seller__seller_reputation__transactions__ratings__negative and 2 other fieldsHigh correlation
seller__seller_reputation__transactions__ratings__positive is highly overall correlated with seller__seller_reputation__transactions__ratings__negative and 3 other fieldsHigh correlation
seller__seller_reputation__transactions__total is highly overall correlated with seller__seller_reputation__transactions__canceled and 6 other fieldsHigh correlation
seller__seller_reputation__metrics__sales__completed is highly overall correlated with seller__seller_reputation__transactions__canceled and 6 other fieldsHigh correlation
seller__seller_reputation__metrics__claims__rate is highly overall correlated with seller__seller_reputation__metrics__claims__value and 4 other fieldsHigh correlation
seller__seller_reputation__metrics__claims__value is highly overall correlated with seller__seller_reputation__transactions__canceled and 10 other fieldsHigh correlation
seller__seller_reputation__metrics__delayed_handling_time__rate is highly overall correlated with seller__seller_reputation__metrics__delayed_handling_time__value and 2 other fieldsHigh correlation
seller__seller_reputation__metrics__delayed_handling_time__value is highly overall correlated with seller__seller_reputation__transactions__canceled and 8 other fieldsHigh correlation
seller__seller_reputation__metrics__cancellations__rate is highly overall correlated with seller__seller_reputation__metrics__claims__rate and 5 other fieldsHigh correlation
seller__seller_reputation__metrics__cancellations__value is highly overall correlated with seller__seller_reputation__transactions__canceled and 8 other fieldsHigh correlation
installments__amount is highly overall correlated with price and 1 other fieldsHigh correlation
days_remaining is highly overall correlated with sold_quantity and 1 other fieldsHigh correlation
years_active is highly overall correlated with seller__id and 1 other fieldsHigh correlation
listing_type_id is highly overall correlated with installments__rate and 1 other fieldsHigh correlation
shipping__logistic_type is highly overall correlated with shipping__mode and 1 other fieldsHigh correlation
shipping__mode is highly overall correlated with shipping__logistic_type and 1 other fieldsHigh correlation
shipping__store_pick_up is highly overall correlated with seller__car_dealer_logoHigh correlation
seller__seller_reputation__level_id is highly overall correlated with seller__seller_reputation__metrics__claims__rate and 4 other fieldsHigh correlation
seller__seller_reputation__power_seller_status is highly overall correlated with seller__seller_reputation__level_id and 1 other fieldsHigh correlation
seller__seller_reputation__metrics__sales__period is highly overall correlated with seller__seller_reputation__metrics__claims__period and 3 other fieldsHigh correlation
seller__seller_reputation__metrics__claims__period is highly overall correlated with seller__seller_reputation__metrics__sales__period and 3 other fieldsHigh correlation
seller__seller_reputation__metrics__delayed_handling_time__period is highly overall correlated with seller__seller_reputation__metrics__sales__period and 3 other fieldsHigh correlation
seller__seller_reputation__metrics__cancellations__period is highly overall correlated with seller__seller_reputation__metrics__sales__period and 3 other fieldsHigh correlation
installments__quantity is highly overall correlated with seller__car_dealer_logoHigh correlation
installments__rate is highly overall correlated with listing_type_id and 1 other fieldsHigh correlation
Categoria is highly overall correlated with seller__car_dealer_logoHigh correlation
seller__car_dealer is highly overall correlated with seller__car_dealer_logoHigh correlation
seller__car_dealer_logo is highly overall correlated with order_backend and 34 other fieldsHigh correlation
listing_type_id is highly imbalanced (59.1%)Imbalance
shipping__mode is highly imbalanced (96.3%)Imbalance
shipping__store_pick_up is highly imbalanced (75.4%)Imbalance
seller__seller_reputation__level_id is highly imbalanced (97.9%)Imbalance
seller__seller_reputation__power_seller_status is highly imbalanced (64.9%)Imbalance
seller__seller_reputation__metrics__sales__period is highly imbalanced (87.0%)Imbalance
seller__seller_reputation__metrics__claims__period is highly imbalanced (87.0%)Imbalance
seller__seller_reputation__metrics__delayed_handling_time__period is highly imbalanced (87.0%)Imbalance
seller__seller_reputation__metrics__cancellations__period is highly imbalanced (87.0%)Imbalance
installments__quantity is highly imbalanced (91.1%)Imbalance
installments__rate is highly imbalanced (59.1%)Imbalance
seller__car_dealer is highly imbalanced (97.2%)Imbalance
shipping__mode has 3998 (20.0%) missing valuesMissing
seller__seller_reputation__power_seller_status has 450 (2.3%) missing valuesMissing
seller__car_dealer has 11994 (60.0%) missing valuesMissing
seller__car_dealer_logo has 19985 (> 99.9%) missing valuesMissing
variation_filters has 16609 (83.1%) missing valuesMissing
seller__seller_reputation__transactions__ratings__neutral is highly skewed (γ1 = 21.96083416)Skewed
df_index is uniformly distributedUniform
id is uniformly distributedUniform
title is uniformly distributedUniform
thumbnail_id is uniformly distributedUniform
permalink is uniformly distributedUniform
Categoria is uniformly distributedUniform
sold_quantity has 997 (5.0%) zerosZeros
seller__seller_reputation__transactions__ratings__negative has 2967 (14.8%) zerosZeros
seller__seller_reputation__transactions__ratings__neutral has 3181 (15.9%) zerosZeros
seller__seller_reputation__metrics__claims__rate has 4621 (23.1%) zerosZeros
seller__seller_reputation__metrics__claims__value has 2090 (10.5%) zerosZeros
seller__seller_reputation__metrics__delayed_handling_time__rate has 2734 (13.7%) zerosZeros
seller__seller_reputation__metrics__delayed_handling_time__value has 2720 (13.6%) zerosZeros
seller__seller_reputation__metrics__cancellations__rate has 8176 (40.9%) zerosZeros
seller__seller_reputation__metrics__cancellations__value has 5160 (25.8%) zerosZeros
years_active has 364 (1.8%) zerosZeros

Reproduction

Analysis started2023-03-24 03:28:28.591767
Analysis finished2023-03-24 03:29:19.029622
Duration50.44 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

df_index
Real number (ℝ)

Distinct4000
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1998.6003
Minimum0
Maximum3999
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size156.3 KiB
2023-03-24T00:29:19.121653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile199.5
Q1999
median1999
Q32998
95-th percentile3798
Maximum3999
Range3999
Interquartile range (IQR)1999

Descriptive statistics

Standard deviation1154.2103
Coefficient of variation (CV)0.57750932
Kurtosis-1.1999978
Mean1998.6003
Median Absolute Deviation (MAD)1000
Skewness1.6643417 × 10-6
Sum39954019
Variance1332201.4
MonotonicityNot monotonic
2023-03-24T00:29:19.233883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
< 0.1%
2670 5
 
< 0.1%
2657 5
 
< 0.1%
2658 5
 
< 0.1%
2659 5
 
< 0.1%
2660 5
 
< 0.1%
2661 5
 
< 0.1%
2662 5
 
< 0.1%
2663 5
 
< 0.1%
2664 5
 
< 0.1%
Other values (3990) 19941
99.7%
ValueCountFrequency (%)
0 5
< 0.1%
1 5
< 0.1%
2 5
< 0.1%
3 5
< 0.1%
4 5
< 0.1%
5 5
< 0.1%
6 5
< 0.1%
7 5
< 0.1%
8 5
< 0.1%
9 5
< 0.1%
ValueCountFrequency (%)
3999 2
 
< 0.1%
3998 2
 
< 0.1%
3997 3
< 0.1%
3996 4
< 0.1%
3995 5
< 0.1%
3994 5
< 0.1%
3993 5
< 0.1%
3992 5
< 0.1%
3991 5
< 0.1%
3990 5
< 0.1%

id
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct19893
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
MLA1292443973
 
2
MLA1150061811
 
2
MLA1123441310
 
2
MLA1150211664
 
2
MLA1259259500
 
2
Other values (19888)
19981 

Length

Max length13
Median length13
Mean length12.593267
Min length12

Characters and Unicode

Total characters251752
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19795 ?
Unique (%)99.0%

Sample

1st rowMLA1112140771
2nd rowMLA1275803410
3rd rowMLA1109575910
4th rowMLA1110677111
5th rowMLA1240728057

Common Values

ValueCountFrequency (%)
MLA1292443973 2
 
< 0.1%
MLA1150061811 2
 
< 0.1%
MLA1123441310 2
 
< 0.1%
MLA1150211664 2
 
< 0.1%
MLA1259259500 2
 
< 0.1%
MLA1234175554 2
 
< 0.1%
MLA634577471 2
 
< 0.1%
MLA1128787633 2
 
< 0.1%
MLA766836895 2
 
< 0.1%
MLA1120234746 2
 
< 0.1%
Other values (19883) 19971
99.9%

Length

2023-03-24T00:29:19.346791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mla1292443973 2
 
< 0.1%
mla1278351969 2
 
< 0.1%
mla1141038876 2
 
< 0.1%
mla1264116706 2
 
< 0.1%
mla1150987101 2
 
< 0.1%
mla1133904147 2
 
< 0.1%
mla1219252190 2
 
< 0.1%
mla1139291510 2
 
< 0.1%
mla1214667239 2
 
< 0.1%
mla1100640679 2
 
< 0.1%
Other values (19883) 19971
99.9%

Most occurring characters

ValueCountFrequency (%)
1 35829
14.2%
2 20085
8.0%
M 19991
7.9%
L 19991
7.9%
A 19991
7.9%
8 18650
 
7.4%
9 18037
 
7.2%
3 17915
 
7.1%
6 16610
 
6.6%
0 16575
 
6.6%
Other values (3) 48078
19.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 191779
76.2%
Uppercase Letter 59973
 
23.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 35829
18.7%
2 20085
10.5%
8 18650
9.7%
9 18037
9.4%
3 17915
9.3%
6 16610
8.7%
0 16575
8.6%
7 16442
8.6%
4 16141
8.4%
5 15495
8.1%
Uppercase Letter
ValueCountFrequency (%)
M 19991
33.3%
L 19991
33.3%
A 19991
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 191779
76.2%
Latin 59973
 
23.8%

Most frequent character per script

Common
ValueCountFrequency (%)
1 35829
18.7%
2 20085
10.5%
8 18650
9.7%
9 18037
9.4%
3 17915
9.3%
6 16610
8.7%
0 16575
8.6%
7 16442
8.6%
4 16141
8.4%
5 15495
8.1%
Latin
ValueCountFrequency (%)
M 19991
33.3%
L 19991
33.3%
A 19991
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 251752
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 35829
14.2%
2 20085
8.0%
M 19991
7.9%
L 19991
7.9%
A 19991
7.9%
8 18650
 
7.4%
9 18037
 
7.2%
3 17915
 
7.1%
6 16610
 
6.6%
0 16575
 
6.6%
Other values (3) 48078
19.1%

title
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct19342
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
Reloj Pulsera Táctil Digital Led Silicona Para Niños Y Niñas
 
5
Caja De Herramientas Juego Llave Tubo Kit 40 Piezas Estuche
 
5
Bolsa De Celular Protectora Agua Waterproof Funda Sumergible
 
4
Reloj Táctil Led Pulsera Digital Silicona Para Niños Y Niñas
 
4
Film Gopro 8 Black Protector Pantalla Lente Vidrio Templado
 
4
Other values (19337)
19969 

Length

Max length200
Median length193
Mean length57.152869
Min length10

Characters and Unicode

Total characters1142543
Distinct characters124
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18769 ?
Unique (%)93.9%

Sample

1st rowPileta Inflable Redonda Bestway Kiddie Lounge 51061 De 61cm X 15cm 21l Azul
2nd rowCry Babies Fantasy Dreamy Imc Toys 99180im
3rd rowJuego De Cartas Desconectados En Palabras
4th rowJuego De Mesa Código Secreto Czech Games Edition Devir
5th rowBellies Bebe Interactivo Beth Edic Especial Int 15145 Orig

Common Values

ValueCountFrequency (%)
Reloj Pulsera Táctil Digital Led Silicona Para Niños Y Niñas 5
 
< 0.1%
Caja De Herramientas Juego Llave Tubo Kit 40 Piezas Estuche 5
 
< 0.1%
Bolsa De Celular Protectora Agua Waterproof Funda Sumergible 4
 
< 0.1%
Reloj Táctil Led Pulsera Digital Silicona Para Niños Y Niñas 4
 
< 0.1%
Film Gopro 8 Black Protector Pantalla Lente Vidrio Templado 4
 
< 0.1%
Juego De Llaves Tubo Laüfer X 40 Piezas Auto-camiones-motos 4
 
< 0.1%
Malla Para Imilab Kw66 Imilab W12 Xiaomi Mi Watch 1.39 4
 
< 0.1%
Pantalla Reflectora 5 En 1 110cm Circular Con Funda Rebote 4
 
< 0.1%
Juego Set Bocallaves Irimo 1/4 1/2 101 Piezas Llaves Tubos 4
 
< 0.1%
Kit Aire 5 Piezas Compresor Gravedad Pistola Pintar Kommberg 4
 
< 0.1%
Other values (19332) 19949
99.8%

Length

2023-03-24T00:29:19.439035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de 7279
 
4.0%
3900
 
2.1%
para 2445
 
1.3%
x 1755
 
1.0%
y 1732
 
0.9%
con 1731
 
0.9%
reloj 1634
 
0.9%
juego 1258
 
0.7%
en 919
 
0.5%
acero 913
 
0.5%
Other values (21795) 159150
87.1%

Most occurring characters

ValueCountFrequency (%)
166774
 
14.6%
a 97724
 
8.6%
o 76948
 
6.7%
e 76426
 
6.7%
r 65233
 
5.7%
i 56960
 
5.0%
l 49674
 
4.3%
t 41083
 
3.6%
n 39947
 
3.5%
s 35799
 
3.1%
Other values (114) 435975
38.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 729695
63.9%
Space Separator 167807
 
14.7%
Uppercase Letter 156053
 
13.7%
Decimal Number 73236
 
6.4%
Other Punctuation 7775
 
0.7%
Dash Punctuation 4861
 
0.4%
Math Symbol 2114
 
0.2%
Close Punctuation 411
 
< 0.1%
Open Punctuation 410
 
< 0.1%
Other Symbol 79
 
< 0.1%
Other values (5) 102
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 97724
13.4%
o 76948
10.5%
e 76426
10.5%
r 65233
8.9%
i 56960
 
7.8%
l 49674
 
6.8%
t 41083
 
5.6%
n 39947
 
5.5%
s 35799
 
4.9%
c 27558
 
3.8%
Other values (33) 162343
22.2%
Uppercase Letter
ValueCountFrequency (%)
P 17279
 
11.1%
C 16838
 
10.8%
D 14172
 
9.1%
M 12491
 
8.0%
A 11659
 
7.5%
S 10275
 
6.6%
B 7208
 
4.6%
T 6802
 
4.4%
R 6736
 
4.3%
E 6556
 
4.2%
Other values (21) 46037
29.5%
Other Punctuation
ValueCountFrequency (%)
/ 2621
33.7%
. 2082
26.8%
, 1640
21.1%
! 705
 
9.1%
' 260
 
3.3%
& 130
 
1.7%
% 109
 
1.4%
* 71
 
0.9%
: 62
 
0.8%
# 46
 
0.6%
Other values (5) 49
 
0.6%
Decimal Number
ValueCountFrequency (%)
0 19879
27.1%
1 12222
16.7%
2 10349
14.1%
5 8012
10.9%
3 5666
 
7.7%
4 4731
 
6.5%
6 4035
 
5.5%
8 3362
 
4.6%
7 2801
 
3.8%
9 2179
 
3.0%
Math Symbol
ValueCountFrequency (%)
+ 2066
97.7%
| 39
 
1.8%
= 6
 
0.3%
× 2
 
0.1%
~ 1
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
° 54
68.4%
® 24
30.4%
© 1
 
1.3%
Modifier Symbol
ValueCountFrequency (%)
´ 34
79.1%
¨ 8
 
18.6%
` 1
 
2.3%
Other Number
ValueCountFrequency (%)
³ 4
66.7%
½ 1
 
16.7%
¾ 1
 
16.7%
Space Separator
ValueCountFrequency (%)
166774
99.4%
  1033
 
0.6%
Close Punctuation
ValueCountFrequency (%)
) 409
99.5%
] 2
 
0.5%
Open Punctuation
ValueCountFrequency (%)
( 408
99.5%
[ 2
 
0.5%
Other Letter
ValueCountFrequency (%)
º 36
97.3%
ª 1
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 4861
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 10
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 885785
77.5%
Common 256758
 
22.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 97724
 
11.0%
o 76948
 
8.7%
e 76426
 
8.6%
r 65233
 
7.4%
i 56960
 
6.4%
l 49674
 
5.6%
t 41083
 
4.6%
n 39947
 
4.5%
s 35799
 
4.0%
c 27558
 
3.1%
Other values (66) 318433
35.9%
Common
ValueCountFrequency (%)
166774
65.0%
0 19879
 
7.7%
1 12222
 
4.8%
2 10349
 
4.0%
5 8012
 
3.1%
3 5666
 
2.2%
- 4861
 
1.9%
4 4731
 
1.8%
6 4035
 
1.6%
8 3362
 
1.3%
Other values (38) 16867
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1133162
99.2%
None 9381
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
166774
 
14.7%
a 97724
 
8.6%
o 76948
 
6.8%
e 76426
 
6.7%
r 65233
 
5.8%
i 56960
 
5.0%
l 49674
 
4.4%
t 41083
 
3.6%
n 39947
 
3.5%
s 35799
 
3.2%
Other values (78) 426594
37.6%
None
ValueCountFrequency (%)
ó 1767
18.8%
á 1668
17.8%
ñ 1665
17.7%
í 1315
14.0%
é 1035
11.0%
  1033
11.0%
ú 349
 
3.7%
Á 110
 
1.2%
ü 64
 
0.7%
° 54
 
0.6%
Other values (26) 321
 
3.4%

thumbnail_id
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct19248
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
825382-MLA42206222187_062020
 
9
901312-MLA49677154007_042022
 
8
724571-MLA50946917429_072022
 
7
842574-MLA40353899541_012020
 
7
625478-MLA51165722301_082022
 
7
Other values (19243)
19953 

Length

Max length28
Median length28
Mean length27.99985
Min length27

Characters and Unicode

Total characters559745
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18634 ?
Unique (%)93.2%

Sample

1st row623284-MLA53605777143_022023
2nd row843969-MLA48269133730_112021
3rd row633026-MLA48408537360_122021
4th row960516-MLA44936648183_022021
5th row994457-MLA49925702800_052022

Common Values

ValueCountFrequency (%)
825382-MLA42206222187_062020 9
 
< 0.1%
901312-MLA49677154007_042022 8
 
< 0.1%
724571-MLA50946917429_072022 7
 
< 0.1%
842574-MLA40353899541_012020 7
 
< 0.1%
625478-MLA51165722301_082022 7
 
< 0.1%
668550-MLA44726560038_012021 6
 
< 0.1%
852216-MLA43553973706_092020 6
 
< 0.1%
792488-MLA46151341533_052021 5
 
< 0.1%
626967-MLA31588684232_072019 5
 
< 0.1%
813901-MLA31911110879_082019 5
 
< 0.1%
Other values (19238) 19926
99.7%

Length

2023-03-24T00:29:19.526787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
825382-mla42206222187_062020 9
 
< 0.1%
901312-mla49677154007_042022 8
 
< 0.1%
724571-mla50946917429_072022 7
 
< 0.1%
625478-mla51165722301_082022 7
 
< 0.1%
842574-mla40353899541_012020 7
 
< 0.1%
668550-mla44726560038_012021 6
 
< 0.1%
852216-mla43553973706_092020 6
 
< 0.1%
792488-mla46151341533_052021 5
 
< 0.1%
626967-mla31588684232_072019 5
 
< 0.1%
813901-mla31911110879_082019 5
 
< 0.1%
Other values (19238) 19926
99.7%

Most occurring characters

ValueCountFrequency (%)
2 80119
14.3%
0 69907
12.5%
1 49778
8.9%
4 41765
7.5%
9 38109
 
6.8%
5 36921
 
6.6%
8 36880
 
6.6%
6 36108
 
6.5%
7 36050
 
6.4%
3 34153
 
6.1%
Other values (8) 99955
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 459790
82.1%
Uppercase Letter 59973
 
10.7%
Dash Punctuation 19991
 
3.6%
Connector Punctuation 19991
 
3.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 80119
17.4%
0 69907
15.2%
1 49778
10.8%
4 41765
9.1%
9 38109
8.3%
5 36921
8.0%
8 36880
8.0%
6 36108
7.9%
7 36050
7.8%
3 34153
7.4%
Uppercase Letter
ValueCountFrequency (%)
L 19991
33.3%
M 19991
33.3%
A 19950
33.3%
U 34
 
0.1%
B 6
 
< 0.1%
C 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 19991
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 19991
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 499772
89.3%
Latin 59973
 
10.7%

Most frequent character per script

Common
ValueCountFrequency (%)
2 80119
16.0%
0 69907
14.0%
1 49778
10.0%
4 41765
8.4%
9 38109
7.6%
5 36921
7.4%
8 36880
7.4%
6 36108
7.2%
7 36050
7.2%
3 34153
6.8%
Other values (2) 39982
8.0%
Latin
ValueCountFrequency (%)
L 19991
33.3%
M 19991
33.3%
A 19950
33.3%
U 34
 
0.1%
B 6
 
< 0.1%
C 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 559745
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 80119
14.3%
0 69907
12.5%
1 49778
8.9%
4 41765
7.5%
9 38109
 
6.8%
5 36921
 
6.6%
8 36880
 
6.6%
6 36108
 
6.5%
7 36050
 
6.4%
3 34153
 
6.1%
Other values (8) 99955
17.9%

listing_type_id
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
gold_special
18354 
gold_pro
 
1637

Length

Max length12
Median length12
Mean length11.672453
Min length8

Characters and Unicode

Total characters233344
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgold_pro
2nd rowgold_special
3rd rowgold_special
4th rowgold_special
5th rowgold_special

Common Values

ValueCountFrequency (%)
gold_special 18354
91.8%
gold_pro 1637
 
8.2%

Length

2023-03-24T00:29:19.612974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-24T00:29:19.692895image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
gold_special 18354
91.8%
gold_pro 1637
 
8.2%

Most occurring characters

ValueCountFrequency (%)
l 38345
16.4%
o 21628
9.3%
g 19991
8.6%
d 19991
8.6%
_ 19991
8.6%
p 19991
8.6%
s 18354
7.9%
e 18354
7.9%
c 18354
7.9%
i 18354
7.9%
Other values (2) 19991
8.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 213353
91.4%
Connector Punctuation 19991
 
8.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 38345
18.0%
o 21628
10.1%
g 19991
9.4%
d 19991
9.4%
p 19991
9.4%
s 18354
8.6%
e 18354
8.6%
c 18354
8.6%
i 18354
8.6%
a 18354
8.6%
Connector Punctuation
ValueCountFrequency (%)
_ 19991
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 213353
91.4%
Common 19991
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 38345
18.0%
o 21628
10.1%
g 19991
9.4%
d 19991
9.4%
p 19991
9.4%
s 18354
8.6%
e 18354
8.6%
c 18354
8.6%
i 18354
8.6%
a 18354
8.6%
Common
ValueCountFrequency (%)
_ 19991
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 233344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 38345
16.4%
o 21628
9.3%
g 19991
8.6%
d 19991
8.6%
_ 19991
8.6%
p 19991
8.6%
s 18354
7.9%
e 18354
7.9%
c 18354
7.9%
i 18354
7.9%
Other values (2) 19991
8.6%

permalink
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct19877
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
https://articulo.mercadolibre.com.ar/MLA-1185200363-base-de-maquillaje-en-polvo-maybelline-fit-me-_JM
 
2
https://www.mercadolibre.com.ar/reparador-de-puntas-semillas-de-lino-30-ml-fidelite-cabellos-danados/p/MLA19765387
 
2
https://www.mercadolibre.com.ar/perfume-mujer-ana-edp-75-ml/p/MLA19720930
 
2
https://articulo.mercadolibre.com.ar/MLA-871575908-repuestos-de-cuchillas-philips-oneblade-qp22051-_JM
 
2
https://www.mercadolibre.com.ar/crema-caviahue-facial-hombre-multiaccion-antiedad-360-45-gr/p/MLA19924535
 
2
Other values (19872)
19981 

Length

Max length359
Median length309
Mean length108.40313
Min length55

Characters and Unicode

Total characters2167087
Distinct characters45
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19763 ?
Unique (%)98.9%

Sample

1st rowhttps://www.mercadolibre.com.ar/pileta-inflable-redonda-bestway-kiddie-lounge-51061-de-61cm-x-15cm-21l-azul/p/MLA15763166
2nd rowhttps://www.mercadolibre.com.ar/cry-babies-fantasy-dreamy-imc-toys-99180im/p/MLA15084428
3rd rowhttps://www.mercadolibre.com.ar/juego-de-cartas-desconectados-en-palabras/p/MLA17840062
4th rowhttps://www.mercadolibre.com.ar/juego-de-mesa-codigo-secreto-czech-games-edition-devir/p/MLA7853901
5th rowhttps://www.mercadolibre.com.ar/bellies-bebe-interactivo-beth-edic-especial-int-15145-orig/p/MLA19765898

Common Values

ValueCountFrequency (%)
https://articulo.mercadolibre.com.ar/MLA-1185200363-base-de-maquillaje-en-polvo-maybelline-fit-me-_JM 2
 
< 0.1%
https://www.mercadolibre.com.ar/reparador-de-puntas-semillas-de-lino-30-ml-fidelite-cabellos-danados/p/MLA19765387 2
 
< 0.1%
https://www.mercadolibre.com.ar/perfume-mujer-ana-edp-75-ml/p/MLA19720930 2
 
< 0.1%
https://articulo.mercadolibre.com.ar/MLA-871575908-repuestos-de-cuchillas-philips-oneblade-qp22051-_JM 2
 
< 0.1%
https://www.mercadolibre.com.ar/crema-caviahue-facial-hombre-multiaccion-antiedad-360-45-gr/p/MLA19924535 2
 
< 0.1%
https://articulo.mercadolibre.com.ar/MLA-897779388-pasta-para-soldar-estano-electronica-150grs-sarasanto-pote-_JM 2
 
< 0.1%
https://articulo.mercadolibre.com.ar/MLA-839471010-fibras-de-keratina-x-275gr-microfibras-capilares-toppik-_JM 2
 
< 0.1%
https://www.mercadolibre.com.ar/mascara-de-pestanas-maybelline-the-falsies-lash-lift-waterproof-029-fl-oz-color-black/p/MLA16265688 2
 
< 0.1%
https://articulo.mercadolibre.com.ar/MLA-868899553-esponja-facial-de-celulosa-comprimida-x12-unidades-oferta-_JM 2
 
< 0.1%
https://www.mercadolibre.com.ar/afeitadora-teknikpro-fading-shaving-negra-220v/p/MLA17374608 2
 
< 0.1%
Other values (19867) 19971
99.9%

Length

2023-03-24T00:29:19.773961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://articulo.mercadolibre.com.ar/mla-1185200363-base-de-maquillaje-en-polvo-maybelline-fit-me-_jm 2
 
< 0.1%
https://articulo.mercadolibre.com.ar/mla-1108457388-collar-hombre-con-dije-cruz-toretto-cadena-acero-quirurgico-_jm 2
 
< 0.1%
https://articulo.mercadolibre.com.ar/mla-792486758-jabon-de-hotel-hotelero-12-grs-blanco-flow-pack-x-1000-u-_jm 2
 
< 0.1%
https://www.mercadolibre.com.ar/paula-cahen-danvers-paula-edt-60ml-para-mujer/p/mla3822099 2
 
< 0.1%
https://www.mercadolibre.com.ar/acondicionador-han-avena-y-miel-500ml-apto-metodo-curly/p/mla19505031 2
 
< 0.1%
https://www.mercadolibre.com.ar/kerastase-chroma-absolu-cuidado-del-color-de-210ml/p/mla19687003 2
 
< 0.1%
https://www.mercadolibre.com.ar/eucerin-sun-gel-crema-toque-seco-tono-medio-spf50-de-x-50ml/p/mla19675708 2
 
< 0.1%
https://articulo.mercadolibre.com.ar/mla-849741708-ambo-zalea-descartable-60cm-x-90cm-x-60-unidades-_jm 2
 
< 0.1%
https://articulo.mercadolibre.com.ar/mla-1125985946-carbones-makita-cb204-ga7020-ga9020-y-varios-modelos-_jm 2
 
< 0.1%
https://articulo.mercadolibre.com.ar/mla-857014102-filtro-anticerumen-para-audifonos-_jm 2
 
< 0.1%
Other values (19867) 19971
99.9%

Most occurring characters

ValueCountFrequency (%)
- 201234
 
9.3%
a 164579
 
7.6%
r 145412
 
6.7%
o 134673
 
6.2%
e 124345
 
5.7%
t 101192
 
4.7%
c 97740
 
4.5%
i 95629
 
4.4%
l 88992
 
4.1%
m 77049
 
3.6%
Other values (35) 936242
43.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1460911
67.4%
Decimal Number 251765
 
11.6%
Dash Punctuation 201234
 
9.3%
Other Punctuation 153349
 
7.1%
Uppercase Letter 86543
 
4.0%
Connector Punctuation 13285
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 164579
11.3%
r 145412
 
10.0%
o 134673
 
9.2%
e 124345
 
8.5%
t 101192
 
6.9%
c 97740
 
6.7%
i 95629
 
6.5%
l 88992
 
6.1%
m 77049
 
5.3%
s 66196
 
4.5%
Other values (16) 365104
25.0%
Decimal Number
ValueCountFrequency (%)
1 43394
17.2%
0 36579
14.5%
2 27398
10.9%
5 23092
9.2%
9 21680
8.6%
8 20718
8.2%
3 20564
8.2%
6 19925
7.9%
4 19223
7.6%
7 19192
7.6%
Uppercase Letter
ValueCountFrequency (%)
M 33276
38.5%
A 19991
23.1%
L 19991
23.1%
J 13285
 
15.4%
Other Punctuation
ValueCountFrequency (%)
/ 73385
47.9%
. 59973
39.1%
: 19991
 
13.0%
Dash Punctuation
ValueCountFrequency (%)
- 201234
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 13285
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1547454
71.4%
Common 619633
28.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 164579
 
10.6%
r 145412
 
9.4%
o 134673
 
8.7%
e 124345
 
8.0%
t 101192
 
6.5%
c 97740
 
6.3%
i 95629
 
6.2%
l 88992
 
5.8%
m 77049
 
5.0%
s 66196
 
4.3%
Other values (20) 451647
29.2%
Common
ValueCountFrequency (%)
- 201234
32.5%
/ 73385
 
11.8%
. 59973
 
9.7%
1 43394
 
7.0%
0 36579
 
5.9%
2 27398
 
4.4%
5 23092
 
3.7%
9 21680
 
3.5%
8 20718
 
3.3%
3 20564
 
3.3%
Other values (5) 91616
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2167087
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 201234
 
9.3%
a 164579
 
7.6%
r 145412
 
6.7%
o 134673
 
6.2%
e 124345
 
5.7%
t 101192
 
4.7%
c 97740
 
4.5%
i 95629
 
4.4%
l 88992
 
4.1%
m 77049
 
3.6%
Other values (35) 936242
43.2%

category_id
Categorical

Distinct725
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
MLA1442
 
1119
MLA1161
 
815
MLA352679
 
738
MLA1436
 
672
MLA392452
 
588
Other values (720)
16059 

Length

Max length9
Median length8
Mean length7.9863939
Min length7

Characters and Unicode

Total characters159656
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique131 ?
Unique (%)0.7%

Sample

1st rowMLA11226
2nd rowMLA2968
3rd rowMLA1161
4th rowMLA1161
5th rowMLA2968

Common Values

ValueCountFrequency (%)
MLA1442 1119
 
5.6%
MLA1161 815
 
4.1%
MLA352679 738
 
3.7%
MLA1436 672
 
3.4%
MLA392452 588
 
2.9%
MLA1271 522
 
2.6%
MLA1634 460
 
2.3%
MLA414007 454
 
2.3%
MLA5232 439
 
2.2%
MLA2968 395
 
2.0%
Other values (715) 13789
69.0%

Length

2023-03-24T00:29:19.859456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mla1442 1119
 
5.6%
mla1161 815
 
4.1%
mla352679 738
 
3.7%
mla1436 672
 
3.4%
mla392452 588
 
2.9%
mla1271 522
 
2.6%
mla1634 460
 
2.3%
mla414007 454
 
2.3%
mla5232 439
 
2.2%
mla2968 395
 
2.0%
Other values (715) 13789
69.0%

Most occurring characters

ValueCountFrequency (%)
M 19991
12.5%
L 19991
12.5%
A 19991
12.5%
1 15475
9.7%
4 14473
9.1%
3 13605
8.5%
2 13089
8.2%
9 8347
5.2%
0 7603
 
4.8%
6 7422
 
4.6%
Other values (3) 19669
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99683
62.4%
Uppercase Letter 59973
37.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 15475
15.5%
4 14473
14.5%
3 13605
13.6%
2 13089
13.1%
9 8347
8.4%
0 7603
7.6%
6 7422
7.4%
5 6987
7.0%
7 6919
6.9%
8 5763
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
M 19991
33.3%
L 19991
33.3%
A 19991
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 99683
62.4%
Latin 59973
37.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 15475
15.5%
4 14473
14.5%
3 13605
13.6%
2 13089
13.1%
9 8347
8.4%
0 7603
7.6%
6 7422
7.4%
5 6987
7.0%
7 6919
6.9%
8 5763
 
5.8%
Latin
ValueCountFrequency (%)
M 19991
33.3%
L 19991
33.3%
A 19991
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 159656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 19991
12.5%
L 19991
12.5%
A 19991
12.5%
1 15475
9.7%
4 14473
9.1%
3 13605
8.5%
2 13089
8.2%
9 8347
5.2%
0 7603
 
4.8%
6 7422
 
4.6%
Other values (3) 19669
12.3%

domain_id
Categorical

Distinct570
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
MLA-WRISTWATCHES
 
1119
MLA-BOARD_GAMES
 
838
MLA-SMARTWATCHES
 
787
MLA-NECKLACES
 
672
MLA-FACIAL_SKIN_CARE_PRODUCTS
 
588
Other values (565)
15987 

Length

Max length58
Median length45
Mean length19.618328
Min length8

Characters and Unicode

Total characters392190
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)0.4%

Sample

1st rowMLA-INFLATABLE_POOLS
2nd rowMLA-DOLLS
3rd rowMLA-BOARD_GAMES
4th rowMLA-BOARD_GAMES
5th rowMLA-DOLLS

Common Values

ValueCountFrequency (%)
MLA-WRISTWATCHES 1119
 
5.6%
MLA-BOARD_GAMES 838
 
4.2%
MLA-SMARTWATCHES 787
 
3.9%
MLA-NECKLACES 672
 
3.4%
MLA-FACIAL_SKIN_CARE_PRODUCTS 588
 
2.9%
MLA-PERFUMES 527
 
2.6%
MLA-PICTURE_FRAMES 482
 
2.4%
MLA-HAIR_SHAMPOOS_AND_CONDITIONERS 454
 
2.3%
MLA-ELECTRIC_DRILLS 439
 
2.2%
MLA-DOLLS 395
 
2.0%
Other values (560) 13690
68.5%

Length

2023-03-24T00:29:19.948071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mla-wristwatches 1119
 
5.6%
mla-board_games 838
 
4.2%
mla-smartwatches 787
 
3.9%
mla-necklaces 672
 
3.4%
mla-facial_skin_care_products 588
 
2.9%
mla-perfumes 527
 
2.6%
mla-picture_frames 482
 
2.4%
mla-hair_shampoos_and_conditioners 454
 
2.3%
mla-electric_drills 439
 
2.2%
mla-dolls 395
 
2.0%
Other values (560) 13690
68.5%

Most occurring characters

ValueCountFrequency (%)
A 49231
12.6%
S 33335
 
8.5%
L 33000
 
8.4%
E 30038
 
7.7%
M 29564
 
7.5%
R 25815
 
6.6%
_ 24024
 
6.1%
- 19991
 
5.1%
T 19430
 
5.0%
C 18758
 
4.8%
Other values (18) 109004
27.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 348175
88.8%
Connector Punctuation 24024
 
6.1%
Dash Punctuation 19991
 
5.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 49231
14.1%
S 33335
9.6%
L 33000
9.5%
E 30038
 
8.6%
M 29564
 
8.5%
R 25815
 
7.4%
T 19430
 
5.6%
C 18758
 
5.4%
I 16999
 
4.9%
O 16031
 
4.6%
Other values (16) 75974
21.8%
Connector Punctuation
ValueCountFrequency (%)
_ 24024
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19991
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 348175
88.8%
Common 44015
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 49231
14.1%
S 33335
9.6%
L 33000
9.5%
E 30038
 
8.6%
M 29564
 
8.5%
R 25815
 
7.4%
T 19430
 
5.6%
C 18758
 
5.4%
I 16999
 
4.9%
O 16031
 
4.6%
Other values (16) 75974
21.8%
Common
ValueCountFrequency (%)
_ 24024
54.6%
- 19991
45.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 392190
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 49231
12.6%
S 33335
 
8.5%
L 33000
 
8.4%
E 30038
 
7.7%
M 29564
 
7.5%
R 25815
 
6.6%
_ 24024
 
6.1%
- 19991
 
5.1%
T 19430
 
5.0%
C 18758
 
4.8%
Other values (18) 109004
27.8%

order_backend
Real number (ℝ)

Distinct50
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.502576
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.3 KiB
2023-03-24T00:29:20.039297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median25
Q338
95-th percentile48
Maximum50
Range49
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.430409
Coefficient of variation (CV)0.56584122
Kurtosis-1.2010277
Mean25.502576
Median Absolute Deviation (MAD)12
Skewness0.00014173366
Sum509822
Variance208.2367
MonotonicityNot monotonic
2023-03-24T00:29:20.140864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 400
 
2.0%
24 400
 
2.0%
2 400
 
2.0%
27 400
 
2.0%
29 400
 
2.0%
32 400
 
2.0%
33 400
 
2.0%
35 400
 
2.0%
36 400
 
2.0%
37 400
 
2.0%
Other values (40) 15991
80.0%
ValueCountFrequency (%)
1 398
2.0%
2 400
2.0%
3 399
2.0%
4 400
2.0%
5 400
2.0%
6 400
2.0%
7 400
2.0%
8 400
2.0%
9 399
2.0%
10 400
2.0%
ValueCountFrequency (%)
50 400
2.0%
49 400
2.0%
48 400
2.0%
47 400
2.0%
46 400
2.0%
45 400
2.0%
44 400
2.0%
43 400
2.0%
42 400
2.0%
41 399
2.0%

price
Real number (ℝ)

Distinct9486
Distinct (%)47.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18294.417
Minimum100
Maximum1888063
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.3 KiB
2023-03-24T00:29:20.237322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile750
Q12199
median5390.53
Q314399
95-th percentile64746.5
Maximum1888063
Range1887963
Interquartile range (IQR)12200

Descriptive statistics

Standard deviation61359.756
Coefficient of variation (CV)3.3540154
Kurtosis269.08498
Mean18294.417
Median Absolute Deviation (MAD)3919.47
Skewness13.752064
Sum3.6572369 × 108
Variance3.7650197 × 109
MonotonicityNot monotonic
2023-03-24T00:29:20.328969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1999 98
 
0.5%
7999 79
 
0.4%
1799 72
 
0.4%
2499 71
 
0.4%
4999 70
 
0.4%
3999 70
 
0.4%
2999 68
 
0.3%
8999 59
 
0.3%
1990 57
 
0.3%
3990 57
 
0.3%
Other values (9476) 19290
96.5%
ValueCountFrequency (%)
100 1
< 0.1%
119 1
< 0.1%
128 1
< 0.1%
129 1
< 0.1%
150 1
< 0.1%
155.6 1
< 0.1%
170 1
< 0.1%
172.62 1
< 0.1%
176 1
< 0.1%
180 1
< 0.1%
ValueCountFrequency (%)
1888062.97 1
< 0.1%
1729107 1
< 0.1%
1603754.59 1
< 0.1%
1593668.1 1
< 0.1%
1500000 1
< 0.1%
1459999 2
< 0.1%
1380000 1
< 0.1%
1255815.23 1
< 0.1%
1249715.83 1
< 0.1%
1170000 1
< 0.1%

sold_quantity
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188.03502
Minimum0
Maximum5000
Zeros997
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size156.3 KiB
2023-03-24T00:29:20.405985image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median50
Q3250
95-th percentile500
Maximum5000
Range5000
Interquartile range (IQR)245

Descriptive statistics

Standard deviation508.0858
Coefficient of variation (CV)2.7020808
Kurtosis75.13215
Mean188.03502
Median Absolute Deviation (MAD)49
Skewness8.2603275
Sum3759008
Variance258151.18
MonotonicityNot monotonic
2023-03-24T00:29:20.475241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
5 3865
19.3%
500 3248
16.2%
50 2416
12.1%
25 2169
10.8%
250 2115
10.6%
100 1419
 
7.1%
150 1034
 
5.2%
0 997
 
5.0%
200 703
 
3.5%
1 547
 
2.7%
Other values (4) 1478
 
7.4%
ValueCountFrequency (%)
0 997
 
5.0%
1 547
 
2.7%
2 485
 
2.4%
3 405
 
2.0%
4 394
 
2.0%
5 3865
19.3%
25 2169
10.8%
50 2416
12.1%
100 1419
 
7.1%
150 1034
 
5.2%
ValueCountFrequency (%)
5000 194
 
1.0%
500 3248
16.2%
250 2115
10.6%
200 703
 
3.5%
150 1034
 
5.2%
100 1419
 
7.1%
50 2416
12.1%
25 2169
10.8%
5 3865
19.3%
4 394
 
2.0%

available_quantity
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean269.11175
Minimum1
Maximum50000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.3 KiB
2023-03-24T00:29:20.535257image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q350
95-th percentile500
Maximum50000
Range49999
Interquartile range (IQR)49

Descriptive statistics

Standard deviation2664.1997
Coefficient of variation (CV)9.8999755
Kurtosis325.21466
Mean269.11175
Median Absolute Deviation (MAD)0
Skewness17.659576
Sum5379813
Variance7097960.3
MonotonicityNot monotonic
2023-03-24T00:29:20.595097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 14363
71.8%
50 1987
 
9.9%
500 1244
 
6.2%
250 714
 
3.6%
100 652
 
3.3%
150 428
 
2.1%
5000 318
 
1.6%
200 231
 
1.2%
50000 54
 
0.3%
ValueCountFrequency (%)
1 14363
71.8%
50 1987
 
9.9%
100 652
 
3.3%
150 428
 
2.1%
200 231
 
1.2%
250 714
 
3.6%
500 1244
 
6.2%
5000 318
 
1.6%
50000 54
 
0.3%
ValueCountFrequency (%)
50000 54
 
0.3%
5000 318
 
1.6%
500 1244
 
6.2%
250 714
 
3.6%
200 231
 
1.2%
150 428
 
2.1%
100 652
 
3.3%
50 1987
 
9.9%
1 14363
71.8%

tags
Categorical

Distinct1166
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
['good_quality_picture', 'good_quality_thumbnail', 'immediate_payment', 'cart_eligible', 'shipping_guaranteed']
2961 
['good_quality_picture', 'good_quality_thumbnail', 'immediate_payment', 'cart_eligible']
1486 
['brand_verified', 'good_quality_picture', 'good_quality_thumbnail', 'immediate_payment', 'cart_eligible', 'shipping_guaranteed']
1291 
['brand_verified', 'good_quality_picture', 'good_quality_thumbnail', 'loyalty_discount_eligible', 'immediate_payment', 'cart_eligible', 'shipping_guaranteed']
 
1071
['good_quality_picture', 'good_quality_thumbnail', 'loyalty_discount_eligible', 'immediate_payment', 'cart_eligible', 'shipping_guaranteed']
 
1070
Other values (1161)
12112 

Length

Max length275
Median length244
Mean length131.57886
Min length38

Characters and Unicode

Total characters2630393
Distinct characters36
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique583 ?
Unique (%)2.9%

Sample

1st row['lightning_deal', 'good_quality_picture', 'good_quality_thumbnail', 'immediate_payment', 'cart_eligible', 'best_seller_candidate', 'shipping_guaranteed']
2nd row['good_quality_picture', 'good_quality_thumbnail', 'immediate_payment', 'cart_eligible', 'best_seller_candidate', 'meli_choice_candidate', 'shipping_guaranteed']
3rd row['good_quality_picture', 'good_quality_thumbnail', 'loyalty_discount_eligible', 'immediate_payment', 'cart_eligible', 'best_seller_candidate', 'shipping_guaranteed']
4th row['good_quality_picture', 'good_quality_thumbnail', 'loyalty_discount_eligible', 'standard_price_by_channel', 'brand_verified', 'immediate_payment', 'cart_eligible', 'shipping_guaranteed']
5th row['good_quality_thumbnail', 'brand_verified', 'good_quality_picture', 'immediate_payment', 'cart_eligible', 'best_seller_candidate', 'shipping_guaranteed']

Common Values

ValueCountFrequency (%)
['good_quality_picture', 'good_quality_thumbnail', 'immediate_payment', 'cart_eligible', 'shipping_guaranteed'] 2961
 
14.8%
['good_quality_picture', 'good_quality_thumbnail', 'immediate_payment', 'cart_eligible'] 1486
 
7.4%
['brand_verified', 'good_quality_picture', 'good_quality_thumbnail', 'immediate_payment', 'cart_eligible', 'shipping_guaranteed'] 1291
 
6.5%
['brand_verified', 'good_quality_picture', 'good_quality_thumbnail', 'loyalty_discount_eligible', 'immediate_payment', 'cart_eligible', 'shipping_guaranteed'] 1071
 
5.4%
['good_quality_picture', 'good_quality_thumbnail', 'loyalty_discount_eligible', 'immediate_payment', 'cart_eligible', 'shipping_guaranteed'] 1070
 
5.4%
['extended_warranty_eligible', 'good_quality_picture', 'good_quality_thumbnail', 'immediate_payment', 'cart_eligible', 'shipping_guaranteed'] 897
 
4.5%
['good_quality_thumbnail', 'good_quality_picture', 'immediate_payment', 'cart_eligible', 'shipping_guaranteed'] 435
 
2.2%
['good_quality_picture', 'good_quality_thumbnail', 'standard_price_by_channel', 'immediate_payment', 'cart_eligible', 'shipping_guaranteed'] 330
 
1.7%
['extended_warranty_eligible', 'good_quality_picture', 'good_quality_thumbnail', 'immediate_payment', 'cart_eligible'] 325
 
1.6%
['good_quality_thumbnail', 'immediate_payment', 'cart_eligible', 'shipping_guaranteed'] 319
 
1.6%
Other values (1156) 9806
49.1%

Length

2023-03-24T00:29:20.680207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
immediate_payment 19991
17.3%
cart_eligible 19737
17.0%
good_quality_thumbnail 18933
16.4%
good_quality_picture 17845
15.4%
shipping_guaranteed 15428
13.3%
brand_verified 6739
 
5.8%
loyalty_discount_eligible 5997
 
5.2%
extended_warranty_eligible 4496
 
3.9%
standard_price_by_channel 2244
 
1.9%
poor_quality_thumbnail 780
 
0.7%
Other values (23) 3603
 
3.1%

Most occurring characters

ValueCountFrequency (%)
i 234680
 
8.9%
' 231586
 
8.8%
e 206829
 
7.9%
a 182156
 
6.9%
t 178095
 
6.8%
_ 171025
 
6.5%
l 136245
 
5.2%
d 107806
 
4.1%
n 101794
 
3.9%
g 100838
 
3.8%
Other values (26) 979339
37.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1995185
75.9%
Other Punctuation 327388
 
12.4%
Connector Punctuation 171025
 
6.5%
Space Separator 95802
 
3.6%
Close Punctuation 19991
 
0.8%
Open Punctuation 19991
 
0.8%
Decimal Number 807
 
< 0.1%
Dash Punctuation 204
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 234680
11.8%
e 206829
 
10.4%
a 182156
 
9.1%
t 178095
 
8.9%
l 136245
 
6.8%
d 107806
 
5.4%
n 101794
 
5.1%
g 100838
 
5.1%
u 98389
 
4.9%
o 90400
 
4.5%
Other values (14) 557953
28.0%
Decimal Number
ValueCountFrequency (%)
3 673
83.4%
6 58
 
7.2%
2 38
 
4.7%
1 37
 
4.6%
4 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
' 231586
70.7%
, 95802
29.3%
Connector Punctuation
ValueCountFrequency (%)
_ 171025
100.0%
Space Separator
ValueCountFrequency (%)
95802
100.0%
Close Punctuation
ValueCountFrequency (%)
] 19991
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 19991
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1995185
75.9%
Common 635208
 
24.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 234680
11.8%
e 206829
 
10.4%
a 182156
 
9.1%
t 178095
 
8.9%
l 136245
 
6.8%
d 107806
 
5.4%
n 101794
 
5.1%
g 100838
 
5.1%
u 98389
 
4.9%
o 90400
 
4.5%
Other values (14) 557953
28.0%
Common
ValueCountFrequency (%)
' 231586
36.5%
_ 171025
26.9%
95802
15.1%
, 95802
15.1%
] 19991
 
3.1%
[ 19991
 
3.1%
3 673
 
0.1%
- 204
 
< 0.1%
6 58
 
< 0.1%
2 38
 
< 0.1%
Other values (2) 38
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2630393
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 234680
 
8.9%
' 231586
 
8.8%
e 206829
 
7.9%
a 182156
 
6.9%
t 178095
 
6.8%
_ 171025
 
6.5%
l 136245
 
5.2%
d 107806
 
4.1%
n 101794
 
3.9%
g 100838
 
3.8%
Other values (26) 979339
37.2%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
cross_docking
9445 
xd_drop_off
5627 
fulfillment
3456 
drop_off
1341 
not_specified
 
84
Other values (2)
 
38

Length

Max length13
Median length11
Mean length11.743935
Min length6

Characters and Unicode

Total characters234773
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfulfillment
2nd rowcross_docking
3rd rowfulfillment
4th rowfulfillment
5th rowfulfillment

Common Values

ValueCountFrequency (%)
cross_docking 9445
47.2%
xd_drop_off 5627
28.1%
fulfillment 3456
 
17.3%
drop_off 1341
 
6.7%
not_specified 84
 
0.4%
default 27
 
0.1%
custom 11
 
0.1%

Length

2023-03-24T00:29:20.762537image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-24T00:29:20.844184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
cross_docking 9445
47.2%
xd_drop_off 5627
28.1%
fulfillment 3456
 
17.3%
drop_off 1341
 
6.7%
not_specified 84
 
0.4%
default 27
 
0.1%
custom 11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 32921
14.0%
d 22151
9.4%
_ 22124
9.4%
f 20959
8.9%
c 18985
8.1%
s 18985
8.1%
r 16413
 
7.0%
i 13069
 
5.6%
n 12985
 
5.5%
l 10395
 
4.4%
Other values (9) 45786
19.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 212649
90.6%
Connector Punctuation 22124
 
9.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 32921
15.5%
d 22151
10.4%
f 20959
9.9%
c 18985
8.9%
s 18985
8.9%
r 16413
7.7%
i 13069
 
6.1%
n 12985
 
6.1%
l 10395
 
4.9%
g 9445
 
4.4%
Other values (8) 36341
17.1%
Connector Punctuation
ValueCountFrequency (%)
_ 22124
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 212649
90.6%
Common 22124
 
9.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 32921
15.5%
d 22151
10.4%
f 20959
9.9%
c 18985
8.9%
s 18985
8.9%
r 16413
7.7%
i 13069
 
6.1%
n 12985
 
6.1%
l 10395
 
4.9%
g 9445
 
4.4%
Other values (8) 36341
17.1%
Common
ValueCountFrequency (%)
_ 22124
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 234773
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 32921
14.0%
d 22151
9.4%
_ 22124
9.4%
f 20959
8.9%
c 18985
8.1%
s 18985
8.1%
r 16413
 
7.0%
i 13069
 
5.6%
n 12985
 
5.5%
l 10395
 
4.4%
Other values (9) 45786
19.5%

shipping__mode
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing3998
Missing (%)20.0%
Memory size156.3 KiB
me2
15871 
not_specified
 
84
me1
 
27
custom
 
11

Length

Max length13
Median length3
Mean length3.0545864
Min length3

Characters and Unicode

Total characters48852
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowme2
2nd rowme2
3rd rowme2
4th rowme2
5th rowme2

Common Values

ValueCountFrequency (%)
me2 15871
79.4%
not_specified 84
 
0.4%
me1 27
 
0.1%
custom 11
 
0.1%
(Missing) 3998
 
20.0%

Length

2023-03-24T00:29:20.920997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-24T00:29:20.997010image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
me2 15871
99.2%
not_specified 84
 
0.5%
me1 27
 
0.2%
custom 11
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e 16066
32.9%
m 15909
32.6%
2 15871
32.5%
i 168
 
0.3%
o 95
 
0.2%
t 95
 
0.2%
s 95
 
0.2%
c 95
 
0.2%
n 84
 
0.2%
_ 84
 
0.2%
Other values (5) 290
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 32870
67.3%
Decimal Number 15898
32.5%
Connector Punctuation 84
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 16066
48.9%
m 15909
48.4%
i 168
 
0.5%
o 95
 
0.3%
t 95
 
0.3%
s 95
 
0.3%
c 95
 
0.3%
n 84
 
0.3%
p 84
 
0.3%
f 84
 
0.3%
Other values (2) 95
 
0.3%
Decimal Number
ValueCountFrequency (%)
2 15871
99.8%
1 27
 
0.2%
Connector Punctuation
ValueCountFrequency (%)
_ 84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 32870
67.3%
Common 15982
32.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 16066
48.9%
m 15909
48.4%
i 168
 
0.5%
o 95
 
0.3%
t 95
 
0.3%
s 95
 
0.3%
c 95
 
0.3%
n 84
 
0.3%
p 84
 
0.3%
f 84
 
0.3%
Other values (2) 95
 
0.3%
Common
ValueCountFrequency (%)
2 15871
99.3%
_ 84
 
0.5%
1 27
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48852
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 16066
32.9%
m 15909
32.6%
2 15871
32.5%
i 168
 
0.3%
o 95
 
0.2%
t 95
 
0.2%
s 95
 
0.2%
c 95
 
0.2%
n 84
 
0.2%
_ 84
 
0.2%
Other values (5) 290
 
0.6%

shipping__store_pick_up
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.6 KiB
False
19175 
True
 
816
ValueCountFrequency (%)
False 19175
95.9%
True 816
 
4.1%
2023-03-24T00:29:21.074526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.6 KiB
False
11935 
True
8056 
ValueCountFrequency (%)
False 11935
59.7%
True 8056
40.3%
2023-03-24T00:29:21.146376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

shipping__tags
Categorical

Distinct119
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
['self_service_in']
6636 
['self_service_in', 'mandatory_free_shipping']
4289 
[]
1404 
['mandatory_free_shipping']
1362 
['MLA-chg-threshold-Feb-23', 'self_service_in']
829 
Other values (114)
5471 

Length

Max length217
Median length197
Mean length32.308539
Min length2

Characters and Unicode

Total characters645880
Distinct characters38
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)0.1%

Sample

1st row['fulfillment', 'self_service_out']
2nd row['self_service_in', 'mandatory_free_shipping']
3rd row['fulfillment', 'self_service_in']
4th row['fulfillment', 'self_service_out', 'mandatory_free_shipping']
5th row['fulfillment', 'self_service_in', 'mandatory_free_shipping']

Common Values

ValueCountFrequency (%)
['self_service_in'] 6636
33.2%
['self_service_in', 'mandatory_free_shipping'] 4289
21.5%
[] 1404
 
7.0%
['mandatory_free_shipping'] 1362
 
6.8%
['MLA-chg-threshold-Feb-23', 'self_service_in'] 829
 
4.1%
['fulfillment', 'self_service_in'] 705
 
3.5%
['fulfillment', 'self_service_out'] 614
 
3.1%
['fulfillment'] 490
 
2.5%
['fulfillment', 'self_service_in', 'mandatory_free_shipping'] 463
 
2.3%
['fulfillment', 'mandatory_free_shipping'] 360
 
1.8%
Other values (109) 2839
14.2%

Length

2023-03-24T00:29:21.231086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
self_service_in 13890
44.3%
mandatory_free_shipping 7767
24.8%
fulfillment 3456
 
11.0%
self_service_out 1938
 
6.2%
mla-chg-threshold-feb-23 1601
 
5.1%
1404
 
4.5%
mla-chg-threshold-ago-22 437
 
1.4%
fs_threshold_mla_change_feb2021 268
 
0.9%
fbm_in_process 138
 
0.4%
fs_removed_by_tagger 130
 
0.4%
Other values (14) 303
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e 71981
 
11.1%
' 59856
 
9.3%
_ 49258
 
7.6%
i 49197
 
7.6%
s 42593
 
6.6%
r 34221
 
5.3%
n 33443
 
5.2%
f 31439
 
4.9%
l 29168
 
4.5%
[ 19991
 
3.1%
Other values (28) 224733
34.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 452307
70.0%
Other Punctuation 71197
 
11.0%
Connector Punctuation 49258
 
7.6%
Open Punctuation 19991
 
3.1%
Close Punctuation 19991
 
3.1%
Space Separator 11341
 
1.8%
Dash Punctuation 8464
 
1.3%
Uppercase Letter 7949
 
1.2%
Decimal Number 5382
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 71981
15.9%
i 49197
10.9%
s 42593
 
9.4%
r 34221
 
7.6%
n 33443
 
7.4%
f 31439
 
7.0%
l 29168
 
6.4%
c 18431
 
4.1%
a 17007
 
3.8%
v 16001
 
3.5%
Other values (13) 108826
24.1%
Decimal Number
ValueCountFrequency (%)
2 3212
59.7%
3 1607
29.9%
1 295
 
5.5%
0 268
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
M 2116
26.6%
L 2116
26.6%
A 2116
26.6%
F 1601
20.1%
Other Punctuation
ValueCountFrequency (%)
' 59856
84.1%
, 11341
 
15.9%
Connector Punctuation
ValueCountFrequency (%)
_ 49258
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 19991
100.0%
Close Punctuation
ValueCountFrequency (%)
] 19991
100.0%
Space Separator
ValueCountFrequency (%)
11341
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8464
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 460256
71.3%
Common 185624
28.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 71981
15.6%
i 49197
10.7%
s 42593
 
9.3%
r 34221
 
7.4%
n 33443
 
7.3%
f 31439
 
6.8%
l 29168
 
6.3%
c 18431
 
4.0%
a 17007
 
3.7%
v 16001
 
3.5%
Other values (17) 116775
25.4%
Common
ValueCountFrequency (%)
' 59856
32.2%
_ 49258
26.5%
[ 19991
 
10.8%
] 19991
 
10.8%
11341
 
6.1%
, 11341
 
6.1%
- 8464
 
4.6%
2 3212
 
1.7%
3 1607
 
0.9%
1 295
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 645880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 71981
 
11.1%
' 59856
 
9.3%
_ 49258
 
7.6%
i 49197
 
7.6%
s 42593
 
6.6%
r 34221
 
5.3%
n 33443
 
5.2%
f 31439
 
4.9%
l 29168
 
4.5%
[ 19991
 
3.1%
Other values (28) 224733
34.8%

seller__id
Real number (ℝ)

Distinct3479
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5264337 × 108
Minimum104859
Maximum1.2716519 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.3 KiB
2023-03-24T00:29:21.325449image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum104859
5-th percentile16053292
Q176914261
median1.8670323 × 108
Q33.245048 × 108
95-th percentile7.7144578 × 108
Maximum1.2716519 × 109
Range1.2715471 × 109
Interquartile range (IQR)2.4759054 × 108

Descriptive statistics

Standard deviation2.4387748 × 108
Coefficient of variation (CV)0.96530332
Kurtosis3.1227287
Mean2.5264337 × 108
Median Absolute Deviation (MAD)1.1414789 × 108
Skewness1.7334504
Sum5.0505936 × 1012
Variance5.9476226 × 1016
MonotonicityNot monotonic
2023-03-24T00:29:21.424039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75694306 213
 
1.1%
216788072 159
 
0.8%
128411524 156
 
0.8%
377840555 139
 
0.7%
196161500 137
 
0.7%
608885756 127
 
0.6%
47316577 125
 
0.6%
460893229 119
 
0.6%
265484143 114
 
0.6%
215976808 109
 
0.5%
Other values (3469) 18593
93.0%
ValueCountFrequency (%)
104859 1
 
< 0.1%
107295 1
 
< 0.1%
111621 68
0.3%
113386 1
 
< 0.1%
125113 3
 
< 0.1%
139927 1
 
< 0.1%
144851 3
 
< 0.1%
192368 1
 
< 0.1%
287318 10
 
0.1%
315471 2
 
< 0.1%
ValueCountFrequency (%)
1271651916 1
 
< 0.1%
1251827186 1
 
< 0.1%
1241249945 1
 
< 0.1%
1238715921 1
 
< 0.1%
1236347534 3
 
< 0.1%
1236086599 2
 
< 0.1%
1235537623 2
 
< 0.1%
1234342080 1
 
< 0.1%
1233632951 1
 
< 0.1%
1230840549 10
0.1%

seller__nickname
Categorical

Distinct3478
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
JUGUETERIA CEBRA
 
213
RELOJESMONTREAL OFICIAL
 
159
KINDERLAND OFICIAL
 
156
FARMACIA SELMA
 
139
ULTRA.CAM
 
137
Other values (3473)
19187 

Length

Max length30
Median length24
Mean length13.498824
Min length3

Characters and Unicode

Total characters269855
Distinct characters48
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1455 ?
Unique (%)7.3%

Sample

1st rowGLOBAL ROSS
2nd rowMORA PAÑALERA
3rd rowENPALABRAS
4th rowINVICTVSJUEGOS
5th rowJUG.OSITO AZUL

Common Values

ValueCountFrequency (%)
JUGUETERIA CEBRA 213
 
1.1%
RELOJESMONTREAL OFICIAL 159
 
0.8%
KINDERLAND OFICIAL 156
 
0.8%
FARMACIA SELMA 139
 
0.7%
ULTRA.CAM 137
 
0.7%
MERCADOLIBRE SUPERMERCADO_AR 127
 
0.6%
PLANETAZENOK 125
 
0.6%
RADIOSPICASA 119
 
0.6%
PIDEWEB 114
 
0.6%
TT-STORE 109
 
0.5%
Other values (3468) 18593
93.0%

Length

2023-03-24T00:29:21.514349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
oficial 456
 
1.7%
farmacia 357
 
1.3%
jugueteria 338
 
1.2%
shop 234
 
0.8%
srl 226
 
0.8%
cebra 213
 
0.8%
online 191
 
0.7%
ferreteria 171
 
0.6%
store 170
 
0.6%
mercadolibre 165
 
0.6%
Other values (3906) 25064
90.9%

Most occurring characters

ValueCountFrequency (%)
A 30407
 
11.3%
E 25829
 
9.6%
R 21449
 
7.9%
O 20645
 
7.7%
I 20293
 
7.5%
S 15602
 
5.8%
T 14874
 
5.5%
L 14050
 
5.2%
N 12541
 
4.6%
C 11552
 
4.3%
Other values (38) 82613
30.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 252172
93.4%
Space Separator 7594
 
2.8%
Decimal Number 4358
 
1.6%
Other Punctuation 2351
 
0.9%
Dash Punctuation 1857
 
0.7%
Connector Punctuation 1522
 
0.6%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 30407
12.1%
E 25829
 
10.2%
R 21449
 
8.5%
O 20645
 
8.2%
I 20293
 
8.0%
S 15602
 
6.2%
T 14874
 
5.9%
L 14050
 
5.6%
N 12541
 
5.0%
C 11552
 
4.6%
Other values (22) 64930
25.7%
Decimal Number
ValueCountFrequency (%)
2 909
20.9%
0 819
18.8%
1 783
18.0%
4 340
 
7.8%
9 293
 
6.7%
7 286
 
6.6%
3 271
 
6.2%
8 224
 
5.1%
5 218
 
5.0%
6 215
 
4.9%
Other Punctuation
ValueCountFrequency (%)
. 2348
99.9%
! 3
 
0.1%
Space Separator
ValueCountFrequency (%)
7594
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1857
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1522
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 252172
93.4%
Common 17683
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 30407
12.1%
E 25829
 
10.2%
R 21449
 
8.5%
O 20645
 
8.2%
I 20293
 
8.0%
S 15602
 
6.2%
T 14874
 
5.9%
L 14050
 
5.6%
N 12541
 
5.0%
C 11552
 
4.6%
Other values (22) 64930
25.7%
Common
ValueCountFrequency (%)
7594
42.9%
. 2348
 
13.3%
- 1857
 
10.5%
_ 1522
 
8.6%
2 909
 
5.1%
0 819
 
4.6%
1 783
 
4.4%
4 340
 
1.9%
9 293
 
1.7%
7 286
 
1.6%
Other values (6) 932
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 269539
99.9%
None 316
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 30407
 
11.3%
E 25829
 
9.6%
R 21449
 
8.0%
O 20645
 
7.7%
I 20293
 
7.5%
S 15602
 
5.8%
T 14874
 
5.5%
L 14050
 
5.2%
N 12541
 
4.7%
C 11552
 
4.3%
Other values (32) 82297
30.5%
None
ValueCountFrequency (%)
É 127
40.2%
Ñ 103
32.6%
Í 53
16.8%
Ó 14
 
4.4%
Á 12
 
3.8%
Ú 7
 
2.2%

seller__tags
Categorical

Distinct175
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
['normal', 'eshop', 'mshops', 'credits_profile', 'messages_as_seller']
3606 
['normal', 'mshops', 'credits_profile', 'messages_as_seller']
2370 
['normal', 'credits_profile', 'mshops', 'messages_as_seller']
2077 
['normal', 'credits_profile', 'messages_as_seller']
1032 
['brand', 'large_seller', 'eshop', 'mshops', 'credits_profile', 'messages_as_seller']
 
966
Other values (170)
9940 

Length

Max length123
Median length118
Mean length74.286229
Min length32

Characters and Unicode

Total characters1485056
Distinct characters32
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)0.1%

Sample

1st row['normal', 'eshop', 'mshops', 'credits_profile', 'messages_as_seller']
2nd row['normal', 'eshop', 'mshops', 'credits_profile', 'messages_as_seller']
3rd row['normal', 'mshops', 'credits_profile', 'messages_as_seller']
4th row['normal', 'eshop', 'credits_profile', 'mshops', 'messages_as_seller']
5th row['brand', 'large_seller', 'mshops', 'credits_profile', 'messages_as_seller']

Common Values

ValueCountFrequency (%)
['normal', 'eshop', 'mshops', 'credits_profile', 'messages_as_seller'] 3606
18.0%
['normal', 'mshops', 'credits_profile', 'messages_as_seller'] 2370
 
11.9%
['normal', 'credits_profile', 'mshops', 'messages_as_seller'] 2077
 
10.4%
['normal', 'credits_profile', 'messages_as_seller'] 1032
 
5.2%
['brand', 'large_seller', 'eshop', 'mshops', 'credits_profile', 'messages_as_seller'] 966
 
4.8%
['normal', 'eshop', 'credits_profile', 'mshops', 'messages_as_seller'] 927
 
4.6%
['brand', 'large_seller', 'credits_profile', 'messages_as_seller'] 660
 
3.3%
['normal', 'credits_profile', 'eshop', 'mshops', 'messages_as_seller'] 518
 
2.6%
['normal', 'credits_priority_4', 'eshop', 'credits_profile', 'mshops', 'messages_as_seller'] 500
 
2.5%
['normal', 'credits_priority_4', 'eshop', 'mshops', 'credits_profile', 'messages_as_seller'] 496
 
2.5%
Other values (165) 6839
34.2%

Length

2023-03-24T00:29:21.603981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
messages_as_seller 19991
20.3%
credits_profile 19657
20.0%
mshops 17072
17.3%
normal 16816
17.1%
eshop 11504
11.7%
credits_priority_4 3643
 
3.7%
brand 3156
 
3.2%
large_seller 3152
 
3.2%
developer 822
 
0.8%
medium_seller 700
 
0.7%
Other values (12) 2018
 
2.0%

Most occurring characters

ValueCountFrequency (%)
' 197062
13.3%
s 174732
11.8%
e 152424
 
10.3%
r 104015
 
7.0%
l 88647
 
6.0%
, 78540
 
5.3%
78540
 
5.3%
_ 74756
 
5.0%
o 71852
 
4.8%
a 64262
 
4.3%
Other values (22) 400226
27.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1011422
68.1%
Other Punctuation 275602
 
18.6%
Space Separator 78540
 
5.3%
Connector Punctuation 74756
 
5.0%
Close Punctuation 19991
 
1.3%
Open Punctuation 19991
 
1.3%
Decimal Number 4754
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 174732
17.3%
e 152424
15.1%
r 104015
10.3%
l 88647
8.8%
o 71852
7.1%
a 64262
 
6.4%
m 55843
 
5.5%
i 55697
 
5.5%
p 53859
 
5.3%
d 30452
 
3.0%
Other values (12) 159639
15.8%
Decimal Number
ValueCountFrequency (%)
4 3643
76.6%
2 566
 
11.9%
3 290
 
6.1%
1 255
 
5.4%
Other Punctuation
ValueCountFrequency (%)
' 197062
71.5%
, 78540
 
28.5%
Space Separator
ValueCountFrequency (%)
78540
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 74756
100.0%
Close Punctuation
ValueCountFrequency (%)
] 19991
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 19991
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1011422
68.1%
Common 473634
31.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 174732
17.3%
e 152424
15.1%
r 104015
10.3%
l 88647
8.8%
o 71852
7.1%
a 64262
 
6.4%
m 55843
 
5.5%
i 55697
 
5.5%
p 53859
 
5.3%
d 30452
 
3.0%
Other values (12) 159639
15.8%
Common
ValueCountFrequency (%)
' 197062
41.6%
, 78540
 
16.6%
78540
 
16.6%
_ 74756
 
15.8%
] 19991
 
4.2%
[ 19991
 
4.2%
4 3643
 
0.8%
2 566
 
0.1%
3 290
 
0.1%
1 255
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1485056
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 197062
13.3%
s 174732
11.8%
e 152424
 
10.3%
r 104015
 
7.0%
l 88647
 
6.0%
, 78540
 
5.3%
78540
 
5.3%
_ 74756
 
5.0%
o 71852
 
4.8%
a 64262
 
4.3%
Other values (22) 400226
27.0%

seller__seller_reputation__level_id
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size156.3 KiB
5_green
19894 
4_light_green
 
59
3_yellow
 
22
2_orange
 
9
1_red
 
3

Length

Max length13
Median length7
Mean length7.0189623
Min length5

Characters and Unicode

Total characters140288
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5_green
2nd row5_green
3rd row5_green
4th row5_green
5th row5_green

Common Values

ValueCountFrequency (%)
5_green 19894
99.5%
4_light_green 59
 
0.3%
3_yellow 22
 
0.1%
2_orange 9
 
< 0.1%
1_red 3
 
< 0.1%
(Missing) 4
 
< 0.1%

Length

2023-03-24T00:29:21.685818image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-24T00:29:21.771632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
5_green 19894
99.5%
4_light_green 59
 
0.3%
3_yellow 22
 
0.1%
2_orange 9
 
< 0.1%
1_red 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 39940
28.5%
_ 20046
14.3%
g 20021
14.3%
r 19965
14.2%
n 19962
14.2%
5 19894
14.2%
l 103
 
0.1%
t 59
 
< 0.1%
h 59
 
< 0.1%
i 59
 
< 0.1%
Other values (9) 180
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 100255
71.5%
Connector Punctuation 20046
 
14.3%
Decimal Number 19987
 
14.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 39940
39.8%
g 20021
20.0%
r 19965
19.9%
n 19962
19.9%
l 103
 
0.1%
t 59
 
0.1%
h 59
 
0.1%
i 59
 
0.1%
o 31
 
< 0.1%
y 22
 
< 0.1%
Other values (3) 34
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
5 19894
99.5%
4 59
 
0.3%
3 22
 
0.1%
2 9
 
< 0.1%
1 3
 
< 0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 20046
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 100255
71.5%
Common 40033
 
28.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 39940
39.8%
g 20021
20.0%
r 19965
19.9%
n 19962
19.9%
l 103
 
0.1%
t 59
 
0.1%
h 59
 
0.1%
i 59
 
0.1%
o 31
 
< 0.1%
y 22
 
< 0.1%
Other values (3) 34
 
< 0.1%
Common
ValueCountFrequency (%)
_ 20046
50.1%
5 19894
49.7%
4 59
 
0.1%
3 22
 
0.1%
2 9
 
< 0.1%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 39940
28.5%
_ 20046
14.3%
g 20021
14.3%
r 19965
14.2%
n 19962
14.2%
5 19894
14.2%
l 103
 
0.1%
t 59
 
< 0.1%
h 59
 
< 0.1%
i 59
 
< 0.1%
Other values (9) 180
 
0.1%

seller__seller_reputation__power_seller_status
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing450
Missing (%)2.3%
Memory size156.3 KiB
platinum
17624 
gold
 
1205
silver
 
712

Length

Max length8
Median length8
Mean length7.6804667
Min length4

Characters and Unicode

Total characters150084
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowplatinum
2nd rowplatinum
3rd rowplatinum
4th rowplatinum
5th rowplatinum

Common Values

ValueCountFrequency (%)
platinum 17624
88.2%
gold 1205
 
6.0%
silver 712
 
3.6%
(Missing) 450
 
2.3%

Length

2023-03-24T00:29:21.850703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-24T00:29:21.932250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
platinum 17624
90.2%
gold 1205
 
6.2%
silver 712
 
3.6%

Most occurring characters

ValueCountFrequency (%)
l 19541
13.0%
i 18336
12.2%
p 17624
11.7%
a 17624
11.7%
t 17624
11.7%
n 17624
11.7%
u 17624
11.7%
m 17624
11.7%
g 1205
 
0.8%
o 1205
 
0.8%
Other values (5) 4053
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 150084
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 19541
13.0%
i 18336
12.2%
p 17624
11.7%
a 17624
11.7%
t 17624
11.7%
n 17624
11.7%
u 17624
11.7%
m 17624
11.7%
g 1205
 
0.8%
o 1205
 
0.8%
Other values (5) 4053
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 150084
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 19541
13.0%
i 18336
12.2%
p 17624
11.7%
a 17624
11.7%
t 17624
11.7%
n 17624
11.7%
u 17624
11.7%
m 17624
11.7%
g 1205
 
0.8%
o 1205
 
0.8%
Other values (5) 4053
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 150084
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 19541
13.0%
i 18336
12.2%
p 17624
11.7%
a 17624
11.7%
t 17624
11.7%
n 17624
11.7%
u 17624
11.7%
m 17624
11.7%
g 1205
 
0.8%
o 1205
 
0.8%
Other values (5) 4053
 
2.7%
Distinct1354
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2999.5315
Minimum0
Maximum51197
Zeros27
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size156.3 KiB
2023-03-24T00:29:22.003151image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34
Q1244
median760
Q32567
95-th percentile11814
Maximum51197
Range51197
Interquartile range (IQR)2323

Descriptive statistics

Standard deviation6856.6771
Coefficient of variation (CV)2.285916
Kurtosis25.163459
Mean2999.5315
Median Absolute Deviation (MAD)648
Skewness4.6791898
Sum59963635
Variance47014021
MonotonicityNot monotonic
2023-03-24T00:29:22.096858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5829 213
 
1.1%
1287 192
 
1.0%
8309 156
 
0.8%
20114 139
 
0.7%
650 138
 
0.7%
35034 127
 
0.6%
5612 125
 
0.6%
9857 119
 
0.6%
11543 114
 
0.6%
2677 109
 
0.5%
Other values (1344) 18559
92.8%
ValueCountFrequency (%)
0 27
0.1%
1 29
0.1%
2 38
0.2%
3 27
0.1%
4 38
0.2%
5 24
0.1%
6 26
0.1%
7 22
0.1%
8 27
0.1%
9 34
0.2%
ValueCountFrequency (%)
51197 4
 
< 0.1%
51193 69
0.3%
49371 106
0.5%
47430 38
 
0.2%
35034 127
0.6%
27314 19
 
0.1%
27012 71
0.4%
22794 108
0.5%
20882 68
0.3%
20870 7
 
< 0.1%
Distinct3044
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55720.105
Minimum2
Maximum844362
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.3 KiB
2023-03-24T00:29:22.187928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile641
Q14864
median13960
Q349863
95-th percentile237259
Maximum844362
Range844360
Interquartile range (IQR)44999

Descriptive statistics

Standard deviation121119.45
Coefficient of variation (CV)2.1737118
Kurtosis18.209423
Mean55720.105
Median Absolute Deviation (MAD)11889
Skewness4.0966841
Sum1.1139006 × 109
Variance1.4669922 × 1010
MonotonicityNot monotonic
2023-03-24T00:29:22.272742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126835 213
 
1.1%
19765 159
 
0.8%
180509 156
 
0.8%
485655 139
 
0.7%
6863 137
 
0.7%
576394 127
 
0.6%
78642 125
 
0.6%
137521 119
 
0.6%
260726 112
 
0.6%
58519 109
 
0.5%
Other values (3034) 18595
93.0%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 3
< 0.1%
11 2
 
< 0.1%
12 5
< 0.1%
13 4
< 0.1%
ValueCountFrequency (%)
844362 4
 
< 0.1%
844185 69
0.3%
759430 5
 
< 0.1%
759391 33
 
0.2%
706813 106
0.5%
672488 108
0.5%
576394 127
0.6%
555429 19
 
0.1%
485655 139
0.7%
436559 13
 
0.1%

seller__seller_reputation__transactions__ratings__negative
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.030768846
Minimum0
Maximum1
Zeros2967
Zeros (%)14.8%
Negative0
Negative (%)0.0%
Memory size156.3 KiB
2023-03-24T00:29:22.357892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01
median0.02
Q30.04
95-th percentile0.09
Maximum1
Range1
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.038850467
Coefficient of variation (CV)1.262656
Kurtosis139.66753
Mean0.030768846
Median Absolute Deviation (MAD)0.01
Skewness7.7953973
Sum615.1
Variance0.0015093588
MonotonicityNot monotonic
2023-03-24T00:29:22.441956image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.01 4557
22.8%
0.02 3408
17.0%
0.03 3079
15.4%
0 2967
14.8%
0.04 1731
 
8.7%
0.05 1498
 
7.5%
0.06 585
 
2.9%
0.07 579
 
2.9%
0.09 516
 
2.6%
0.08 322
 
1.6%
Other values (33) 749
 
3.7%
ValueCountFrequency (%)
0 2967
14.8%
0.01 4557
22.8%
0.02 3408
17.0%
0.03 3079
15.4%
0.04 1731
 
8.7%
0.05 1498
 
7.5%
0.06 585
 
2.9%
0.07 579
 
2.9%
0.08 322
 
1.6%
0.09 516
 
2.6%
ValueCountFrequency (%)
1 6
< 0.1%
0.57 3
< 0.1%
0.5 6
< 0.1%
0.48 1
 
< 0.1%
0.44 3
< 0.1%
0.42 3
< 0.1%
0.4 1
 
< 0.1%
0.39 1
 
< 0.1%
0.37 4
< 0.1%
0.36 2
 
< 0.1%

seller__seller_reputation__transactions__ratings__neutral
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.019113601
Minimum0
Maximum1
Zeros3181
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size156.3 KiB
2023-03-24T00:29:22.519949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01
median0.02
Q30.02
95-th percentile0.04
Maximum1
Range1
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.03797533
Coefficient of variation (CV)1.9868224
Kurtosis557.68635
Mean0.019113601
Median Absolute Deviation (MAD)0.01
Skewness21.960834
Sum382.1
Variance0.0014421257
MonotonicityNot monotonic
2023-03-24T00:29:22.594038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.02 6147
30.7%
0.01 5901
29.5%
0 3181
15.9%
0.03 2994
15.0%
0.04 1099
 
5.5%
0.05 341
 
1.7%
0.06 175
 
0.9%
0.07 34
 
0.2%
0.08 32
 
0.2%
1 25
 
0.1%
Other values (16) 62
 
0.3%
ValueCountFrequency (%)
0 3181
15.9%
0.01 5901
29.5%
0.02 6147
30.7%
0.03 2994
15.0%
0.04 1099
 
5.5%
0.05 341
 
1.7%
0.06 175
 
0.9%
0.07 34
 
0.2%
0.08 32
 
0.2%
0.09 17
 
0.1%
ValueCountFrequency (%)
1 25
0.1%
0.5 1
 
< 0.1%
0.45 2
 
< 0.1%
0.36 1
 
< 0.1%
0.33 1
 
< 0.1%
0.25 1
 
< 0.1%
0.24 1
 
< 0.1%
0.19 2
 
< 0.1%
0.18 2
 
< 0.1%
0.17 1
 
< 0.1%
Distinct48
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95011755
Minimum0
Maximum1
Zeros31
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size156.3 KiB
2023-03-24T00:29:22.678119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.88
Q10.94
median0.96
Q30.98
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.057154042
Coefficient of variation (CV)0.060154706
Kurtosis124.21091
Mean0.95011755
Median Absolute Deviation (MAD)0.02
Skewness-8.4992921
Sum18993.8
Variance0.0032665845
MonotonicityNot monotonic
2023-03-24T00:29:23.287909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.98 3004
15.0%
0.97 2766
13.8%
0.96 2569
12.9%
0.94 1802
9.0%
0.99 1741
8.7%
0.95 1737
8.7%
1 1434
7.2%
0.93 1324
6.6%
0.92 997
 
5.0%
0.91 514
 
2.6%
Other values (38) 2103
10.5%
ValueCountFrequency (%)
0 31
0.2%
0.29 1
 
< 0.1%
0.32 1
 
< 0.1%
0.43 2
 
< 0.1%
0.45 6
 
< 0.1%
0.5 1
 
< 0.1%
0.52 1
 
< 0.1%
0.55 2
 
< 0.1%
0.56 3
 
< 0.1%
0.57 7
 
< 0.1%
ValueCountFrequency (%)
1 1434
7.2%
0.99 1741
8.7%
0.98 3004
15.0%
0.97 2766
13.8%
0.96 2569
12.9%
0.95 1737
8.7%
0.94 1802
9.0%
0.93 1324
6.6%
0.92 997
 
5.0%
0.91 514
 
2.6%
Distinct3054
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58719.637
Minimum2
Maximum895559
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.3 KiB
2023-03-24T00:29:23.378730image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile671.5
Q15071.5
median14723
Q352723
95-th percentile249073
Maximum895559
Range895557
Interquartile range (IQR)47651.5

Descriptive statistics

Standard deviation127768.97
Coefficient of variation (CV)2.1759155
Kurtosis18.398744
Mean58719.637
Median Absolute Deviation (MAD)12567
Skewness4.1126814
Sum1.1738643 × 109
Variance1.6324909 × 1010
MonotonicityNot monotonic
2023-03-24T00:29:23.462008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132664 213
 
1.1%
21052 159
 
0.8%
188818 156
 
0.8%
505769 139
 
0.7%
7513 137
 
0.7%
611428 127
 
0.6%
84254 125
 
0.6%
147378 119
 
0.6%
272269 112
 
0.6%
61196 109
 
0.5%
Other values (3044) 18595
93.0%
ValueCountFrequency (%)
2 1
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
8 2
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
12 4
< 0.1%
13 1
 
< 0.1%
14 2
< 0.1%
15 4
< 0.1%
ValueCountFrequency (%)
895559 4
 
< 0.1%
895378 69
0.3%
806860 5
 
< 0.1%
806821 33
 
0.2%
756184 106
0.5%
695282 108
0.5%
611428 127
0.6%
582743 19
 
0.1%
505769 139
0.7%
457441 13
 
0.1%

seller__seller_reputation__metrics__sales__period
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
60 days
19632 
365 days
 
359

Length

Max length8
Median length7
Mean length7.0179581
Min length7

Characters and Unicode

Total characters140296
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row60 days
2nd row60 days
3rd row60 days
4th row60 days
5th row60 days

Common Values

ValueCountFrequency (%)
60 days 19632
98.2%
365 days 359
 
1.8%

Length

2023-03-24T00:29:23.537076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-24T00:29:23.605783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
days 19991
50.0%
60 19632
49.1%
365 359
 
0.9%

Most occurring characters

ValueCountFrequency (%)
6 19991
14.2%
19991
14.2%
d 19991
14.2%
a 19991
14.2%
y 19991
14.2%
s 19991
14.2%
0 19632
14.0%
3 359
 
0.3%
5 359
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 79964
57.0%
Decimal Number 40341
28.8%
Space Separator 19991
 
14.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 19991
49.6%
0 19632
48.7%
3 359
 
0.9%
5 359
 
0.9%
Lowercase Letter
ValueCountFrequency (%)
d 19991
25.0%
a 19991
25.0%
y 19991
25.0%
s 19991
25.0%
Space Separator
ValueCountFrequency (%)
19991
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 79964
57.0%
Common 60332
43.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 19991
33.1%
19991
33.1%
0 19632
32.5%
3 359
 
0.6%
5 359
 
0.6%
Latin
ValueCountFrequency (%)
d 19991
25.0%
a 19991
25.0%
y 19991
25.0%
s 19991
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140296
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 19991
14.2%
19991
14.2%
d 19991
14.2%
a 19991
14.2%
y 19991
14.2%
s 19991
14.2%
0 19632
14.0%
3 359
 
0.3%
5 359
 
0.3%
Distinct2113
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8961.1585
Minimum2
Maximum125430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.3 KiB
2023-03-24T00:29:23.677966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile156
Q1800
median2498
Q38352
95-th percentile39258
Maximum125430
Range125428
Interquartile range (IQR)7552

Descriptive statistics

Standard deviation17961.209
Coefficient of variation (CV)2.0043401
Kurtosis17.011397
Mean8961.1585
Median Absolute Deviation (MAD)2075
Skewness3.8680571
Sum1.7914252 × 108
Variance3.2260504 × 108
MonotonicityNot monotonic
2023-03-24T00:29:23.778006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22626 213
 
1.1%
3268 178
 
0.9%
1023 156
 
0.8%
30143 156
 
0.8%
85379 139
 
0.7%
52506 127
 
0.6%
11257 125
 
0.6%
37109 119
 
0.6%
41551 112
 
0.6%
10315 109
 
0.5%
Other values (2103) 18557
92.8%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 3
< 0.1%
11 2
 
< 0.1%
12 5
< 0.1%
13 4
< 0.1%
ValueCountFrequency (%)
125430 4
 
< 0.1%
125253 69
0.3%
113015 106
0.5%
101843 5
 
< 0.1%
101804 33
 
0.2%
93700 108
0.5%
85379 139
0.7%
71138 19
 
0.1%
66899 1
 
< 0.1%
66870 13
 
0.1%

seller__seller_reputation__metrics__claims__period
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
60 days
19632 
365 days
 
359

Length

Max length8
Median length7
Mean length7.0179581
Min length7

Characters and Unicode

Total characters140296
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row60 days
2nd row60 days
3rd row60 days
4th row60 days
5th row60 days

Common Values

ValueCountFrequency (%)
60 days 19632
98.2%
365 days 359
 
1.8%

Length

2023-03-24T00:29:23.866423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-24T00:29:23.942061image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
days 19991
50.0%
60 19632
49.1%
365 359
 
0.9%

Most occurring characters

ValueCountFrequency (%)
6 19991
14.2%
19991
14.2%
d 19991
14.2%
a 19991
14.2%
y 19991
14.2%
s 19991
14.2%
0 19632
14.0%
3 359
 
0.3%
5 359
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 79964
57.0%
Decimal Number 40341
28.8%
Space Separator 19991
 
14.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 19991
49.6%
0 19632
48.7%
3 359
 
0.9%
5 359
 
0.9%
Lowercase Letter
ValueCountFrequency (%)
d 19991
25.0%
a 19991
25.0%
y 19991
25.0%
s 19991
25.0%
Space Separator
ValueCountFrequency (%)
19991
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 79964
57.0%
Common 60332
43.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 19991
33.1%
19991
33.1%
0 19632
32.5%
3 359
 
0.6%
5 359
 
0.6%
Latin
ValueCountFrequency (%)
d 19991
25.0%
a 19991
25.0%
y 19991
25.0%
s 19991
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140296
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 19991
14.2%
19991
14.2%
d 19991
14.2%
a 19991
14.2%
y 19991
14.2%
s 19991
14.2%
0 19632
14.0%
3 359
 
0.3%
5 359
 
0.3%

seller__seller_reputation__metrics__claims__rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct208
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0050930169
Minimum0
Maximum0.0495
Zeros4621
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size156.3 KiB
2023-03-24T00:29:24.034721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0008
median0.0047
Q30.0078
95-th percentile0.0132
Maximum0.0495
Range0.0495
Interquartile range (IQR)0.007

Descriptive statistics

Standard deviation0.0044829796
Coefficient of variation (CV)0.88022084
Kurtosis2.8251997
Mean0.0050930169
Median Absolute Deviation (MAD)0.0034
Skewness1.0082534
Sum101.8145
Variance2.0097106 × 10-5
MonotonicityNot monotonic
2023-03-24T00:29:24.132558image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4621
 
23.1%
0.0054 480
 
2.4%
0.0045 356
 
1.8%
0.0091 330
 
1.7%
0.0049 302
 
1.5%
0.0052 298
 
1.5%
0.0051 293
 
1.5%
0.0077 269
 
1.3%
0.0061 248
 
1.2%
0.0034 243
 
1.2%
Other values (198) 12551
62.8%
ValueCountFrequency (%)
0 4621
23.1%
0.0001 57
 
0.3%
0.0002 6
 
< 0.1%
0.0003 126
 
0.6%
0.0004 16
 
0.1%
0.0005 13
 
0.1%
0.0006 43
 
0.2%
0.0007 87
 
0.4%
0.0008 82
 
0.4%
0.0009 34
 
0.2%
ValueCountFrequency (%)
0.0495 5
< 0.1%
0.0417 1
 
< 0.1%
0.037 1
 
< 0.1%
0.0285 1
 
< 0.1%
0.0279 6
< 0.1%
0.0246 1
 
< 0.1%
0.0242 1
 
< 0.1%
0.024 1
 
< 0.1%
0.0224 1
 
< 0.1%
0.022 1
 
< 0.1%

seller__seller_reputation__metrics__claims__value
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct192
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.873543
Minimum0
Maximum1062
Zeros2090
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size156.3 KiB
2023-03-24T00:29:24.228258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q342
95-th percentile311
Maximum1062
Range1062
Interquartile range (IQR)39

Descriptive statistics

Standard deviation126.83815
Coefficient of variation (CV)2.2301785
Kurtosis23.478899
Mean56.873543
Median Absolute Deviation (MAD)11
Skewness4.3140343
Sum1136959
Variance16087.917
MonotonicityNot monotonic
2023-03-24T00:29:24.319384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2090
 
10.5%
1 1393
 
7.0%
2 1138
 
5.7%
3 913
 
4.6%
4 873
 
4.4%
6 735
 
3.7%
5 607
 
3.0%
9 605
 
3.0%
13 574
 
2.9%
8 405
 
2.0%
Other values (182) 10658
53.3%
ValueCountFrequency (%)
0 2090
10.5%
1 1393
7.0%
2 1138
5.7%
3 913
4.6%
4 873
4.4%
5 607
 
3.0%
6 735
 
3.7%
7 400
 
2.0%
8 405
 
2.0%
9 605
 
3.0%
ValueCountFrequency (%)
1062 38
 
0.2%
1032 73
0.4%
644 106
0.5%
624 27
 
0.1%
559 41
 
0.2%
558 30
 
0.2%
481 108
0.5%
425 30
 
0.2%
422 10
 
0.1%
410 10
 
0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
60 days
19632 
365 days
 
359

Length

Max length8
Median length7
Mean length7.0179581
Min length7

Characters and Unicode

Total characters140296
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row60 days
2nd row60 days
3rd row60 days
4th row60 days
5th row60 days

Common Values

ValueCountFrequency (%)
60 days 19632
98.2%
365 days 359
 
1.8%

Length

2023-03-24T00:29:24.397566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-24T00:29:24.464803image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
days 19991
50.0%
60 19632
49.1%
365 359
 
0.9%

Most occurring characters

ValueCountFrequency (%)
6 19991
14.2%
19991
14.2%
d 19991
14.2%
a 19991
14.2%
y 19991
14.2%
s 19991
14.2%
0 19632
14.0%
3 359
 
0.3%
5 359
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 79964
57.0%
Decimal Number 40341
28.8%
Space Separator 19991
 
14.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 19991
49.6%
0 19632
48.7%
3 359
 
0.9%
5 359
 
0.9%
Lowercase Letter
ValueCountFrequency (%)
d 19991
25.0%
a 19991
25.0%
y 19991
25.0%
s 19991
25.0%
Space Separator
ValueCountFrequency (%)
19991
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 79964
57.0%
Common 60332
43.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 19991
33.1%
19991
33.1%
0 19632
32.5%
3 359
 
0.6%
5 359
 
0.6%
Latin
ValueCountFrequency (%)
d 19991
25.0%
a 19991
25.0%
y 19991
25.0%
s 19991
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140296
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 19991
14.2%
19991
14.2%
d 19991
14.2%
a 19991
14.2%
y 19991
14.2%
s 19991
14.2%
0 19632
14.0%
3 359
 
0.3%
5 359
 
0.3%

seller__seller_reputation__metrics__delayed_handling_time__rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct756
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.016894963
Minimum0
Maximum0.348
Zeros2734
Zeros (%)13.7%
Negative0
Negative (%)0.0%
Memory size156.3 KiB
2023-03-24T00:29:24.534724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0017
median0.0067
Q30.021
95-th percentile0.0716
Maximum0.348
Range0.348
Interquartile range (IQR)0.0193

Descriptive statistics

Standard deviation0.025240444
Coefficient of variation (CV)1.4939627
Kurtosis9.8195125
Mean0.016894963
Median Absolute Deviation (MAD)0.0063
Skewness2.6545739
Sum337.7472
Variance0.00063708004
MonotonicityNot monotonic
2023-03-24T00:29:24.622323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2734
 
13.7%
0.0015 306
 
1.5%
0.0067 283
 
1.4%
0.0028 244
 
1.2%
0.0033 240
 
1.2%
0.0013 237
 
1.2%
0.0017 236
 
1.2%
0.0138 235
 
1.2%
0.0025 234
 
1.2%
0.0024 213
 
1.1%
Other values (746) 15029
75.2%
ValueCountFrequency (%)
0 2734
13.7%
0.0001 120
 
0.6%
0.0002 127
 
0.6%
0.0003 33
 
0.2%
0.0004 130
 
0.7%
0.0005 96
 
0.5%
0.0006 53
 
0.3%
0.0007 169
 
0.8%
0.0008 188
 
0.9%
0.0009 211
 
1.1%
ValueCountFrequency (%)
0.348 2
< 0.1%
0.2444 3
< 0.1%
0.2 1
 
< 0.1%
0.1705 1
 
< 0.1%
0.1538 1
 
< 0.1%
0.1506 1
 
< 0.1%
0.1505 1
 
< 0.1%
0.15 2
< 0.1%
0.1467 1
 
< 0.1%
0.1441 1
 
< 0.1%

seller__seller_reputation__metrics__delayed_handling_time__value
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct255
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.09384
Minimum0
Maximum3638
Zeros2720
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size156.3 KiB
2023-03-24T00:29:24.713462image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median15
Q372
95-th percentile466
Maximum3638
Range3638
Interquartile range (IQR)69

Descriptive statistics

Standard deviation399.96762
Coefficient of variation (CV)3.2492903
Kurtosis48.252985
Mean123.09384
Median Absolute Deviation (MAD)15
Skewness6.4646566
Sum2460769
Variance159974.1
MonotonicityNot monotonic
2023-03-24T00:29:24.800449image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2720
 
13.6%
1 1327
 
6.6%
2 729
 
3.6%
5 580
 
2.9%
3 578
 
2.9%
4 551
 
2.8%
11 497
 
2.5%
10 479
 
2.4%
8 425
 
2.1%
9 390
 
2.0%
Other values (245) 11715
58.6%
ValueCountFrequency (%)
0 2720
13.6%
1 1327
6.6%
2 729
 
3.6%
3 578
 
2.9%
4 551
 
2.8%
5 580
 
2.9%
6 384
 
1.9%
7 276
 
1.4%
8 425
 
2.1%
9 390
 
2.0%
ValueCountFrequency (%)
3638 156
0.8%
2020 45
 
0.2%
1983 75
0.4%
1954 1
 
< 0.1%
1912 1
 
< 0.1%
1843 13
 
0.1%
1720 32
 
0.2%
1689 30
 
0.2%
1345 73
0.4%
1222 1
 
< 0.1%

seller__seller_reputation__metrics__cancellations__period
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
60 days
19632 
365 days
 
359

Length

Max length8
Median length7
Mean length7.0179581
Min length7

Characters and Unicode

Total characters140296
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row60 days
2nd row60 days
3rd row60 days
4th row60 days
5th row60 days

Common Values

ValueCountFrequency (%)
60 days 19632
98.2%
365 days 359
 
1.8%

Length

2023-03-24T00:29:24.890994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-24T00:29:24.957919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
days 19991
50.0%
60 19632
49.1%
365 359
 
0.9%

Most occurring characters

ValueCountFrequency (%)
6 19991
14.2%
19991
14.2%
d 19991
14.2%
a 19991
14.2%
y 19991
14.2%
s 19991
14.2%
0 19632
14.0%
3 359
 
0.3%
5 359
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 79964
57.0%
Decimal Number 40341
28.8%
Space Separator 19991
 
14.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 19991
49.6%
0 19632
48.7%
3 359
 
0.9%
5 359
 
0.9%
Lowercase Letter
ValueCountFrequency (%)
d 19991
25.0%
a 19991
25.0%
y 19991
25.0%
s 19991
25.0%
Space Separator
ValueCountFrequency (%)
19991
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 79964
57.0%
Common 60332
43.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 19991
33.1%
19991
33.1%
0 19632
32.5%
3 359
 
0.6%
5 359
 
0.6%
Latin
ValueCountFrequency (%)
d 19991
25.0%
a 19991
25.0%
y 19991
25.0%
s 19991
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140296
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 19991
14.2%
19991
14.2%
d 19991
14.2%
a 19991
14.2%
y 19991
14.2%
s 19991
14.2%
0 19632
14.0%
3 359
 
0.3%
5 359
 
0.3%

seller__seller_reputation__metrics__cancellations__rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct186
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.002946671
Minimum0
Maximum0.0909
Zeros8176
Zeros (%)40.9%
Negative0
Negative (%)0.0%
Memory size156.3 KiB
2023-03-24T00:29:25.028735image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0014
Q30.0047
95-th percentile0.0111
Maximum0.0909
Range0.0909
Interquartile range (IQR)0.0047

Descriptive statistics

Standard deviation0.003848325
Coefficient of variation (CV)1.3059907
Kurtosis16.920183
Mean0.002946671
Median Absolute Deviation (MAD)0.0014
Skewness2.1725813
Sum58.9069
Variance1.4809606 × 10-5
MonotonicityNot monotonic
2023-03-24T00:29:25.115925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8176
40.9%
0.0011 431
 
2.2%
0.0032 386
 
1.9%
0.0027 348
 
1.7%
0.004 338
 
1.7%
0.0022 309
 
1.5%
0.0029 296
 
1.5%
0.0024 271
 
1.4%
0.0009 258
 
1.3%
0.0056 236
 
1.2%
Other values (176) 8942
44.7%
ValueCountFrequency (%)
0 8176
40.9%
0.0002 47
 
0.2%
0.0003 172
 
0.9%
0.0004 108
 
0.5%
0.0005 17
 
0.1%
0.0006 90
 
0.5%
0.0007 88
 
0.4%
0.0008 85
 
0.4%
0.0009 258
 
1.3%
0.001 211
 
1.1%
ValueCountFrequency (%)
0.0909 1
 
< 0.1%
0.0394 2
 
< 0.1%
0.0322 1
 
< 0.1%
0.0303 1
 
< 0.1%
0.0295 1
 
< 0.1%
0.0293 1
 
< 0.1%
0.0283 1
 
< 0.1%
0.0279 1
 
< 0.1%
0.0277 1
 
< 0.1%
0.0263 9
< 0.1%

seller__seller_reputation__metrics__cancellations__value
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct138
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.253514
Minimum0
Maximum863
Zeros5160
Zeros (%)25.8%
Negative0
Negative (%)0.0%
Memory size156.3 KiB
2023-03-24T00:29:25.204622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q325
95-th percentile121.5
Maximum863
Range863
Interquartile range (IQR)25

Descriptive statistics

Standard deviation84.459241
Coefficient of variation (CV)2.7023918
Kurtosis43.047464
Mean31.253514
Median Absolute Deviation (MAD)5
Skewness5.8944939
Sum624789
Variance7133.3634
MonotonicityNot monotonic
2023-03-24T00:29:25.290586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5160
25.8%
1 1588
 
7.9%
2 1392
 
7.0%
4 913
 
4.6%
3 782
 
3.9%
6 619
 
3.1%
5 568
 
2.8%
10 494
 
2.5%
9 464
 
2.3%
8 327
 
1.6%
Other values (128) 7684
38.4%
ValueCountFrequency (%)
0 5160
25.8%
1 1588
 
7.9%
2 1392
 
7.0%
3 782
 
3.9%
4 913
 
4.6%
5 568
 
2.8%
6 619
 
3.1%
7 257
 
1.3%
8 327
 
1.6%
9 464
 
2.3%
ValueCountFrequency (%)
863 71
0.4%
498 139
0.7%
488 106
0.5%
420 27
 
0.1%
390 13
 
0.1%
310 27
 
0.1%
279 10
 
0.1%
275 16
 
0.1%
269 38
 
0.2%
254 2
 
< 0.1%

installments__quantity
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size156.3 KiB
6.0
19597 
3.0
 
383
12.0
 
7

Length

Max length4
Median length3
Mean length3.0003502
Min length3

Characters and Unicode

Total characters59968
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6.0
2nd row6.0
3rd row6.0
4th row6.0
5th row6.0

Common Values

ValueCountFrequency (%)
6.0 19597
98.0%
3.0 383
 
1.9%
12.0 7
 
< 0.1%
(Missing) 4
 
< 0.1%

Length

2023-03-24T00:29:25.366765image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-24T00:29:25.435696image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
6.0 19597
98.0%
3.0 383
 
1.9%
12.0 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 19987
33.3%
0 19987
33.3%
6 19597
32.7%
3 383
 
0.6%
1 7
 
< 0.1%
2 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39981
66.7%
Other Punctuation 19987
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19987
50.0%
6 19597
49.0%
3 383
 
1.0%
1 7
 
< 0.1%
2 7
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 19987
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 59968
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 19987
33.3%
0 19987
33.3%
6 19597
32.7%
3 383
 
0.6%
1 7
 
< 0.1%
2 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59968
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 19987
33.3%
0 19987
33.3%
6 19597
32.7%
3 383
 
0.6%
1 7
 
< 0.1%
2 7
 
< 0.1%

installments__amount
Real number (ℝ)

Distinct9717
Distinct (%)48.6%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4283.3715
Minimum24.72
Maximum360984.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.3 KiB
2023-03-24T00:29:25.505558image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum24.72
5-th percentile184.45
Q1541.48
median1310.18
Q33482.305
95-th percentile14834.678
Maximum360984.75
Range360960.03
Interquartile range (IQR)2940.825

Descriptive statistics

Standard deviation14012.304
Coefficient of variation (CV)3.2713258
Kurtosis232.04543
Mean4283.3715
Median Absolute Deviation (MAD)943.31
Skewness13.056013
Sum85611746
Variance1.9634466 × 108
MonotonicityNot monotonic
2023-03-24T00:29:25.593488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
494.25 97
 
0.5%
1977.75 77
 
0.4%
617.88 71
 
0.4%
444.8 71
 
0.4%
741.5 69
 
0.3%
988.75 68
 
0.3%
1236 68
 
0.3%
492.03 60
 
0.3%
986.53 58
 
0.3%
618.12 55
 
0.3%
Other values (9707) 19293
96.5%
ValueCountFrequency (%)
24.72 1
< 0.1%
29.42 1
< 0.1%
31.65 1
< 0.1%
31.9 1
< 0.1%
37.09 1
< 0.1%
38.47 1
< 0.1%
41.67 2
< 0.1%
42.03 1
< 0.1%
42.68 1
< 0.1%
43.52 1
< 0.1%
ValueCountFrequency (%)
360984.75 2
< 0.1%
341355.33 1
< 0.1%
341205 1
< 0.1%
335739.67 1
< 0.1%
310500.32 1
< 0.1%
308992.24 1
< 0.1%
278564.54 1
< 0.1%
276919.75 2
< 0.1%
271076.33 2
< 0.1%
270172.67 1
< 0.1%

installments__rate
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Memory size156.3 KiB
48.35
18351 
0.0
 
1636

Length

Max length5
Median length5
Mean length4.8362936
Min length3

Characters and Unicode

Total characters96663
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row48.35
3rd row48.35
4th row48.35
5th row48.35

Common Values

ValueCountFrequency (%)
48.35 18351
91.8%
0.0 1636
 
8.2%
(Missing) 4
 
< 0.1%

Length

2023-03-24T00:29:25.677855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-24T00:29:25.751852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
48.35 18351
91.8%
0.0 1636
 
8.2%

Most occurring characters

ValueCountFrequency (%)
. 19987
20.7%
4 18351
19.0%
8 18351
19.0%
3 18351
19.0%
5 18351
19.0%
0 3272
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 76676
79.3%
Other Punctuation 19987
 
20.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 18351
23.9%
8 18351
23.9%
3 18351
23.9%
5 18351
23.9%
0 3272
 
4.3%
Other Punctuation
ValueCountFrequency (%)
. 19987
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 96663
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 19987
20.7%
4 18351
19.0%
8 18351
19.0%
3 18351
19.0%
5 18351
19.0%
0 3272
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 96663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 19987
20.7%
4 18351
19.0%
8 18351
19.0%
3 18351
19.0%
5 18351
19.0%
0 3272
 
3.4%

days_remaining
Real number (ℝ)

Distinct2089
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6761.4681
Minimum4653
Maximum7299
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.3 KiB
2023-03-24T00:29:25.821062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum4653
5-th percentile5528
Q16532
median6951
Q37186
95-th percentile7277
Maximum7299
Range2646
Interquartile range (IQR)654

Descriptive statistics

Standard deviation563.2434
Coefficient of variation (CV)0.083301939
Kurtosis2.187885
Mean6761.4681
Median Absolute Deviation (MAD)270
Skewness-1.5713953
Sum1.3516851 × 108
Variance317243.13
MonotonicityNot monotonic
2023-03-24T00:29:25.910486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7223 108
 
0.5%
7235 102
 
0.5%
7272 101
 
0.5%
7228 98
 
0.5%
7231 96
 
0.5%
7230 96
 
0.5%
7186 95
 
0.5%
7295 92
 
0.5%
7221 90
 
0.5%
7194 89
 
0.4%
Other values (2079) 19024
95.2%
ValueCountFrequency (%)
4653 14
0.1%
4669 1
 
< 0.1%
4672 1
 
< 0.1%
4722 5
 
< 0.1%
4723 4
 
< 0.1%
4725 2
 
< 0.1%
4727 1
 
< 0.1%
4728 1
 
< 0.1%
4730 1
 
< 0.1%
4734 4
 
< 0.1%
ValueCountFrequency (%)
7299 36
 
0.2%
7298 21
 
0.1%
7297 17
 
0.1%
7296 33
 
0.2%
7295 92
0.5%
7294 78
0.4%
7293 59
0.3%
7292 67
0.3%
7291 56
0.3%
7290 14
 
0.1%

years_active
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4668601
Minimum0
Maximum23
Zeros364
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size156.3 KiB
2023-03-24T00:29:25.986731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median7
Q313
95-th percentile18
Maximum23
Range23
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.4932484
Coefficient of variation (CV)0.64879404
Kurtosis-0.73109531
Mean8.4668601
Median Absolute Deviation (MAD)4
Skewness0.54984548
Sum169261
Variance30.175778
MonotonicityNot monotonic
2023-03-24T00:29:26.056393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
6 1763
 
8.8%
7 1630
 
8.2%
4 1598
 
8.0%
3 1545
 
7.7%
5 1532
 
7.7%
2 1389
 
6.9%
10 1167
 
5.8%
8 938
 
4.7%
1 841
 
4.2%
9 817
 
4.1%
Other values (14) 6771
33.9%
ValueCountFrequency (%)
0 364
 
1.8%
1 841
4.2%
2 1389
6.9%
3 1545
7.7%
4 1598
8.0%
5 1532
7.7%
6 1763
8.8%
7 1630
8.2%
8 938
4.7%
9 817
4.1%
ValueCountFrequency (%)
23 78
 
0.4%
22 26
 
0.1%
21 113
 
0.6%
20 349
1.7%
19 432
2.2%
18 665
3.3%
17 585
2.9%
16 760
3.8%
15 748
3.7%
14 603
3.0%

Categoria
Categorical

HIGH CORRELATION  UNIFORM 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
juguetes
4000 
elementos de fotografia
4000 
accesorios
3998 
herramientas
3997 
cuidado de la piel
3996 

Length

Max length23
Median length12
Mean length14.19999
Min length8

Characters and Unicode

Total characters283872
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowjuguetes
2nd rowjuguetes
3rd rowjuguetes
4th rowjuguetes
5th rowjuguetes

Common Values

ValueCountFrequency (%)
juguetes 4000
20.0%
elementos de fotografia 4000
20.0%
accesorios 3998
20.0%
herramientas 3997
20.0%
cuidado de la piel 3996
20.0%

Length

2023-03-24T00:29:26.128140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-24T00:29:26.210996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
de 7996
20.0%
juguetes 4000
10.0%
elementos 4000
10.0%
fotografia 4000
10.0%
accesorios 3998
10.0%
herramientas 3997
10.0%
cuidado 3996
10.0%
la 3996
10.0%
piel 3996
10.0%

Most occurring characters

ValueCountFrequency (%)
e 43984
15.5%
a 27984
9.9%
o 23992
 
8.5%
s 19993
 
7.0%
19988
 
7.0%
i 19987
 
7.0%
t 15997
 
5.6%
r 15992
 
5.6%
d 15988
 
5.6%
u 11996
 
4.2%
Other values (9) 67971
23.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 263884
93.0%
Space Separator 19988
 
7.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 43984
16.7%
a 27984
10.6%
o 23992
9.1%
s 19993
 
7.6%
i 19987
 
7.6%
t 15997
 
6.1%
r 15992
 
6.1%
d 15988
 
6.1%
u 11996
 
4.5%
c 11992
 
4.5%
Other values (8) 55979
21.2%
Space Separator
ValueCountFrequency (%)
19988
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 263884
93.0%
Common 19988
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 43984
16.7%
a 27984
10.6%
o 23992
9.1%
s 19993
 
7.6%
i 19987
 
7.6%
t 15997
 
6.1%
r 15992
 
6.1%
d 15988
 
6.1%
u 11996
 
4.5%
c 11992
 
4.5%
Other values (8) 55979
21.2%
Common
ValueCountFrequency (%)
19988
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 283872
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 43984
15.5%
a 27984
9.9%
o 23992
 
8.5%
s 19993
 
7.0%
19988
 
7.0%
i 19987
 
7.0%
t 15997
 
5.6%
r 15992
 
5.6%
d 15988
 
5.6%
u 11996
 
4.2%
Other values (9) 67971
23.9%

seller__car_dealer
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing11994
Missing (%)60.0%
Memory size156.3 KiB
False
7974 
True
 
23
(Missing)
11994 
ValueCountFrequency (%)
False 7974
39.9%
True 23
 
0.1%
(Missing) 11994
60.0%
2023-03-24T00:29:26.291999image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

seller__car_dealer_logo
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)66.7%
Missing19985
Missing (%)> 99.9%
Memory size156.3 KiB
https://http2.mlstatic.com/storage/vis-accounts/92653704_vip-e71e4bf7-e955-45ea-99ea-9d3a188d80c1.jpg
https://resources.mlstatic.com/classifieds_accounts/MLA_car_dealer/166819630_vip.jpg
https://img.mlstatic.com/org-img/mktlogos/newAdmLogos/MOT/MLA/logo24214132.jpg
https://resources.mlstatic.com/classifieds_accounts/MLA_car_dealer/195514361_vip.jpg

Length

Max length101
Median length92.5
Mean length91.5
Min length78

Characters and Unicode

Total characters549
Distinct characters42
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)50.0%

Sample

1st rowhttps://resources.mlstatic.com/classifieds_accounts/MLA_car_dealer/166819630_vip.jpg
2nd rowhttps://http2.mlstatic.com/storage/vis-accounts/92653704_vip-e71e4bf7-e955-45ea-99ea-9d3a188d80c1.jpg
3rd rowhttps://img.mlstatic.com/org-img/mktlogos/newAdmLogos/MOT/MLA/logo24214132.jpg
4th rowhttps://http2.mlstatic.com/storage/vis-accounts/92653704_vip-e71e4bf7-e955-45ea-99ea-9d3a188d80c1.jpg
5th rowhttps://http2.mlstatic.com/storage/vis-accounts/92653704_vip-e71e4bf7-e955-45ea-99ea-9d3a188d80c1.jpg

Common Values

ValueCountFrequency (%)
https://http2.mlstatic.com/storage/vis-accounts/92653704_vip-e71e4bf7-e955-45ea-99ea-9d3a188d80c1.jpg 3
 
< 0.1%
https://resources.mlstatic.com/classifieds_accounts/MLA_car_dealer/166819630_vip.jpg 1
 
< 0.1%
https://img.mlstatic.com/org-img/mktlogos/newAdmLogos/MOT/MLA/logo24214132.jpg 1
 
< 0.1%
https://resources.mlstatic.com/classifieds_accounts/MLA_car_dealer/195514361_vip.jpg 1
 
< 0.1%
(Missing) 19985
> 99.9%

Length

2023-03-24T00:29:26.351906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-24T00:29:26.430743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
https://http2.mlstatic.com/storage/vis-accounts/92653704_vip-e71e4bf7-e955-45ea-99ea-9d3a188d80c1.jpg 3
50.0%
https://resources.mlstatic.com/classifieds_accounts/mla_car_dealer/166819630_vip.jpg 1
 
16.7%
https://img.mlstatic.com/org-img/mktlogos/newadmlogos/mot/mla/logo24214132.jpg 1
 
16.7%
https://resources.mlstatic.com/classifieds_accounts/mla_car_dealer/195514361_vip.jpg 1
 
16.7%

Most occurring characters

ValueCountFrequency (%)
t 39
 
7.1%
s 35
 
6.4%
/ 33
 
6.0%
c 31
 
5.6%
e 29
 
5.3%
a 29
 
5.3%
o 23
 
4.2%
i 20
 
3.6%
p 20
 
3.6%
- 19
 
3.5%
Other values (32) 271
49.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 338
61.6%
Decimal Number 110
 
20.0%
Other Punctuation 57
 
10.4%
Dash Punctuation 19
 
3.5%
Uppercase Letter 14
 
2.6%
Connector Punctuation 11
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 39
11.5%
s 35
10.4%
c 31
 
9.2%
e 29
 
8.6%
a 29
 
8.6%
o 23
 
6.8%
i 20
 
5.9%
p 20
 
5.9%
m 16
 
4.7%
g 15
 
4.4%
Other values (12) 81
24.0%
Decimal Number
ValueCountFrequency (%)
9 17
15.5%
1 16
14.5%
5 14
12.7%
4 12
10.9%
8 10
9.1%
3 9
8.2%
7 9
8.2%
2 9
8.2%
6 7
6.4%
0 7
6.4%
Uppercase Letter
ValueCountFrequency (%)
M 4
28.6%
L 4
28.6%
A 4
28.6%
O 1
 
7.1%
T 1
 
7.1%
Other Punctuation
ValueCountFrequency (%)
/ 33
57.9%
. 18
31.6%
: 6
 
10.5%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 352
64.1%
Common 197
35.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 39
11.1%
s 35
 
9.9%
c 31
 
8.8%
e 29
 
8.2%
a 29
 
8.2%
o 23
 
6.5%
i 20
 
5.7%
p 20
 
5.7%
m 16
 
4.5%
g 15
 
4.3%
Other values (17) 95
27.0%
Common
ValueCountFrequency (%)
/ 33
16.8%
- 19
9.6%
. 18
9.1%
9 17
8.6%
1 16
8.1%
5 14
 
7.1%
4 12
 
6.1%
_ 11
 
5.6%
8 10
 
5.1%
3 9
 
4.6%
Other values (5) 38
19.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 549
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 39
 
7.1%
s 35
 
6.4%
/ 33
 
6.0%
c 31
 
5.6%
e 29
 
5.3%
a 29
 
5.3%
o 23
 
4.2%
i 20
 
3.6%
p 20
 
3.6%
- 19
 
3.5%
Other values (32) 271
49.4%

variation_filters
Categorical

HIGH CARDINALITY  MISSING 

Distinct214
Distinct (%)6.3%
Missing16609
Missing (%)83.1%
Memory size156.3 KiB
['COLOR']
1172 
['STRAP_COLOR', 'BEZEL_COLOR', 'BACKGROUND_COLOR']
309 
['STRAP_COLOR']
296 
['CASE_COLOR']
155 
['CASE_COLOR', 'WRISTBAND_COLOR']
148 
Other values (209)
1302 

Length

Max length1773
Median length630
Mean length32.090479
Min length8

Characters and Unicode

Total characters108530
Distinct characters62
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique107 ?
Unique (%)3.2%

Sample

1st row['CASE_COLOR']
2nd row['COLOR']
3rd row['COLOR']
4th row['COLOR']
5th row['COLOR']

Common Values

ValueCountFrequency (%)
['COLOR'] 1172
 
5.9%
['STRAP_COLOR', 'BEZEL_COLOR', 'BACKGROUND_COLOR'] 309
 
1.5%
['STRAP_COLOR'] 296
 
1.5%
['CASE_COLOR'] 155
 
0.8%
['CASE_COLOR', 'WRISTBAND_COLOR'] 148
 
0.7%
['DIAMETER', 'LENGTH'] 106
 
0.5%
['COLOR', 'SIZE'] 71
 
0.4%
['WRISTBAND_COLOR'] 71
 
0.4%
['DIAMETER'] 70
 
0.4%
['STRAP_COLOR', 'BEZEL_COLOR', 'BACKGROUND_COLOR', 'STRAP_COLOR', 'BEZEL_COLOR', 'BACKGROUND_COLOR'] 68
 
0.3%
Other values (204) 916
 
4.6%
(Missing) 16609
83.1%

Length

2023-03-24T00:29:26.539060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
color 2654
31.3%
strap_color 1220
14.4%
bezel_color 1080
12.8%
background_color 1066
12.6%
width 493
 
5.8%
case_color 423
 
5.0%
wristband_color 351
 
4.1%
diameter 243
 
2.9%
length 161
 
1.9%
largo 98
 
1.2%
Other values (84) 678
 
8.0%

Most occurring characters

ValueCountFrequency (%)
' 16406
15.1%
O 14743
13.6%
R 9763
 
9.0%
C 8580
 
7.9%
L 8306
 
7.7%
5085
 
4.7%
, 4821
 
4.4%
_ 4295
 
4.0%
A 3436
 
3.2%
E 3397
 
3.1%
Other values (52) 29698
27.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 68403
63.0%
Other Punctuation 21240
 
19.6%
Space Separator 5085
 
4.7%
Connector Punctuation 4295
 
4.0%
Open Punctuation 3382
 
3.1%
Close Punctuation 3382
 
3.1%
Lowercase Letter 2738
 
2.5%
Decimal Number 5
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 14743
21.6%
R 9763
14.3%
C 8580
12.5%
L 8306
12.1%
A 3436
 
5.0%
E 3397
 
5.0%
T 2610
 
3.8%
B 2515
 
3.7%
D 2206
 
3.2%
S 2183
 
3.2%
Other values (15) 10664
15.6%
Lowercase Letter
ValueCountFrequency (%)
a 568
20.7%
e 365
13.3%
o 327
11.9%
l 279
10.2%
r 261
9.5%
d 252
9.2%
n 142
 
5.2%
g 117
 
4.3%
i 94
 
3.4%
m 75
 
2.7%
Other values (15) 258
9.4%
Other Punctuation
ValueCountFrequency (%)
' 16406
77.2%
, 4821
 
22.7%
: 10
 
< 0.1%
. 3
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
4 2
40.0%
2 1
20.0%
1 1
20.0%
9 1
20.0%
Space Separator
ValueCountFrequency (%)
5085
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4295
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 3382
100.0%
Close Punctuation
ValueCountFrequency (%)
] 3382
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 71141
65.5%
Common 37389
34.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 14743
20.7%
R 9763
13.7%
C 8580
12.1%
L 8306
11.7%
A 3436
 
4.8%
E 3397
 
4.8%
T 2610
 
3.7%
B 2515
 
3.5%
D 2206
 
3.1%
S 2183
 
3.1%
Other values (40) 13402
18.8%
Common
ValueCountFrequency (%)
' 16406
43.9%
5085
 
13.6%
, 4821
 
12.9%
_ 4295
 
11.5%
[ 3382
 
9.0%
] 3382
 
9.0%
: 10
 
< 0.1%
. 3
 
< 0.1%
4 2
 
< 0.1%
2 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 108498
> 99.9%
None 32
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 16406
15.1%
O 14743
13.6%
R 9763
 
9.0%
C 8580
 
7.9%
L 8306
 
7.7%
5085
 
4.7%
, 4821
 
4.4%
_ 4295
 
4.0%
A 3436
 
3.2%
E 3397
 
3.1%
Other values (48) 29666
27.3%
None
ValueCountFrequency (%)
ñ 28
87.5%
Ñ 2
 
6.2%
ó 1
 
3.1%
ú 1
 
3.1%

Interactions

2023-03-24T00:29:15.681025image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:35.746780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-03-24T00:28:43.752441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-03-24T00:28:52.370271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:54.182891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:55.983857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:57.955323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:59.766081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:01.584274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:03.378823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:05.515690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:07.467857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:09.465127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:11.321585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:13.548889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:15.298028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:17.075709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:37.331498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:39.226156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:41.072193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:43.014628image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:44.854567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:46.806144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:48.620284image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:50.439216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:52.450397image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:54.265969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:56.065228image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:58.033116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:59.847625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:01.668219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:03.458157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:05.600791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:07.582274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:09.545433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:11.400214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:13.635471image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:15.375723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:17.150783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:37.414086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:39.302405image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:41.150988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:43.092065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:44.930678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:46.884276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:48.698658image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:50.521299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:52.527770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:54.345383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:56.142798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:58.110091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:59.928846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:01.747899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:03.779022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:05.683013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:07.688418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:09.622436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:11.475820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:13.715922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:15.451220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:17.229943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:37.501532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:39.385125image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:41.241445image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:43.174491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:45.015338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:46.966775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:48.784266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:50.786978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:52.612978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:54.430769image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:56.228587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:58.192865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:00.017195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:01.832729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:03.862998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:05.771507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:07.821265image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:09.706267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:11.557590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:13.801552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:15.534958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:17.302271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:37.580278image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:39.474115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:41.327325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:43.248834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:45.089595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:47.044677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:48.860608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:50.869855image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:52.689591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:54.507987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:56.304389image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:28:58.267100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:00.106994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:01.909420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:03.938379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:05.851958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:07.908775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:09.782541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:11.633500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:13.879067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-24T00:29:15.607374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-03-24T00:29:26.661426image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
df_indexorder_backendpricesold_quantityavailable_quantityseller__idseller__seller_reputation__transactions__canceledseller__seller_reputation__transactions__completedseller__seller_reputation__transactions__ratings__negativeseller__seller_reputation__transactions__ratings__neutralseller__seller_reputation__transactions__ratings__positiveseller__seller_reputation__transactions__totalseller__seller_reputation__metrics__sales__completedseller__seller_reputation__metrics__claims__rateseller__seller_reputation__metrics__claims__valueseller__seller_reputation__metrics__delayed_handling_time__rateseller__seller_reputation__metrics__delayed_handling_time__valueseller__seller_reputation__metrics__cancellations__rateseller__seller_reputation__metrics__cancellations__valueinstallments__amountdays_remainingyears_activelisting_type_idshipping__logistic_typeshipping__modeshipping__store_pick_upshipping__free_shippingseller__seller_reputation__level_idseller__seller_reputation__power_seller_statusseller__seller_reputation__metrics__sales__periodseller__seller_reputation__metrics__claims__periodseller__seller_reputation__metrics__delayed_handling_time__periodseller__seller_reputation__metrics__cancellations__periodinstallments__quantityinstallments__rateCategoriaseller__car_dealerseller__car_dealer_logo
df_index1.0000.013-0.063-0.071-0.039-0.033-0.015-0.015-0.032-0.0080.031-0.015-0.0210.013-0.0130.0270.0070.0090.001-0.066-0.0740.0350.0180.0570.0160.0190.0490.0140.0130.0390.0390.0390.0390.0080.0180.0000.0670.000
order_backend0.0131.000-0.001-0.006-0.0090.0010.0000.001-0.003-0.0080.0040.0010.0010.0060.001-0.002-0.0000.0090.008-0.0010.002-0.0020.0150.0070.0000.0000.0000.0070.0000.0000.0000.0000.0000.0110.0150.0000.0360.667
price-0.063-0.0011.000-0.233-0.186-0.083-0.043-0.0800.010-0.0450.010-0.078-0.1100.087-0.033-0.014-0.0700.015-0.0250.9970.0470.0850.1010.0150.0000.0000.1370.0300.0600.1360.1360.1360.1360.0700.1000.0880.0001.000
sold_quantity-0.071-0.006-0.2331.0000.343-0.0490.2670.2890.0440.105-0.0570.2880.2690.0180.197-0.0650.129-0.0370.084-0.229-0.5510.0440.0140.0760.0000.0120.0350.0000.0230.0090.0090.0090.0090.0000.0140.0830.0001.000
available_quantity-0.039-0.009-0.1860.3431.0000.0330.2360.2330.0940.166-0.1260.2340.2370.1060.2280.0770.1990.0330.129-0.187-0.126-0.0430.0070.0340.0000.0000.0310.0000.0070.0000.0000.0000.0000.0000.0070.0420.0001.000
seller__id-0.0330.001-0.083-0.0490.0331.000-0.054-0.0640.2000.048-0.187-0.063-0.018-0.013-0.0110.012-0.034-0.054-0.080-0.0840.197-0.9580.0530.1400.0440.1430.0940.0660.0350.0420.0420.0420.0420.0450.0530.1500.0790.707
seller__seller_reputation__transactions__canceled-0.0150.000-0.0430.2670.236-0.0541.0000.9780.4670.410-0.4630.9810.9550.4320.8930.0190.6230.4250.702-0.042-0.0390.0550.0580.1560.0120.2790.1010.0000.0900.0490.0490.0490.0490.0750.0580.1580.0001.000
seller__seller_reputation__transactions__completed-0.0150.001-0.0800.2890.233-0.0640.9781.0000.3960.358-0.3901.0000.9720.3450.857-0.0120.6100.3830.678-0.078-0.0370.0650.0730.1560.0000.3130.1390.0000.0970.0530.0530.0530.0530.0690.0730.1730.0001.000
seller__seller_reputation__transactions__ratings__negative-0.032-0.0030.0100.0440.0940.2000.4670.3961.0000.526-0.9290.4000.4290.4600.5380.2220.4360.3750.4460.0100.143-0.2110.0000.0640.0000.0230.0100.1900.0500.0670.0670.0670.0670.0000.0000.0510.0001.000
seller__seller_reputation__transactions__ratings__neutral-0.008-0.008-0.0450.1050.1660.0480.4100.3580.5261.000-0.7660.3610.3750.4020.4790.2510.4580.3330.432-0.0460.014-0.0620.0350.0430.0000.0000.0000.0350.0770.1850.1850.1850.1850.0480.0350.0190.0001.000
seller__seller_reputation__transactions__ratings__positive0.0310.0040.010-0.057-0.126-0.187-0.463-0.390-0.929-0.7661.000-0.394-0.424-0.470-0.544-0.257-0.465-0.375-0.4590.010-0.1290.2010.0400.0610.0000.0300.0620.2290.0670.1720.1720.1720.1720.0320.0400.0870.0001.000
seller__seller_reputation__transactions__total-0.0150.001-0.0780.2880.234-0.0630.9811.0000.4000.361-0.3941.0000.9720.3500.860-0.0100.6120.3860.680-0.076-0.0370.0640.0680.1570.0000.3250.1380.0000.0960.0530.0530.0530.0530.0700.0680.1760.0001.000
seller__seller_reputation__metrics__sales__completed-0.0210.001-0.1100.2690.237-0.0180.9550.9720.4290.375-0.4240.9721.0000.3570.8790.0060.6390.3960.694-0.1080.0100.0160.0800.1580.0000.3240.1580.0000.1050.0580.0580.0580.0580.0780.0800.1770.0001.000
seller__seller_reputation__metrics__claims__rate0.0130.0060.0870.0180.106-0.0130.4320.3450.4600.402-0.4700.3500.3571.0000.6850.3490.4930.5200.5030.083-0.005-0.0050.0450.0930.0170.1490.1160.5470.1800.1140.1140.1140.1140.0420.0450.0760.0000.707
seller__seller_reputation__metrics__claims__value-0.0130.001-0.0330.1970.228-0.0110.8930.8570.5380.479-0.5440.8600.8790.6851.0000.2000.7250.5370.770-0.0340.0040.0030.0570.1450.0000.2860.1140.0000.0930.0510.0510.0510.0510.0400.0570.1290.0001.000
seller__seller_reputation__metrics__delayed_handling_time__rate0.027-0.002-0.014-0.0650.0770.0120.019-0.0120.2220.251-0.257-0.0100.0060.3490.2001.0000.6960.3360.308-0.0150.065-0.0300.0000.0920.0000.2550.0630.5170.0840.1110.1110.1110.1110.0000.0000.0950.0701.000
seller__seller_reputation__metrics__delayed_handling_time__value0.007-0.000-0.0700.1290.199-0.0340.6230.6100.4360.458-0.4650.6120.6390.4930.7250.6961.0000.5440.740-0.0700.0330.0210.0200.0920.0130.4460.0700.0000.0600.0310.0310.0310.0310.0230.0200.1450.0001.000
seller__seller_reputation__metrics__cancellations__rate0.0090.0090.015-0.0370.033-0.0540.4250.3830.3750.333-0.3750.3860.3960.5200.5370.3360.5441.0000.8350.0120.0650.0460.0200.0490.0000.0100.0290.5250.0450.1350.1350.1350.1350.0050.0200.0670.0001.000
seller__seller_reputation__metrics__cancellations__value0.0010.008-0.0250.0840.129-0.0800.7020.6780.4460.432-0.4590.6800.6940.5030.7700.3080.7400.8351.000-0.0270.0450.0780.0420.1000.0300.2050.0920.0000.0650.0340.0340.0340.0340.0350.0420.1170.0001.000
installments__amount-0.066-0.0010.997-0.229-0.187-0.084-0.042-0.0780.010-0.0460.010-0.076-0.1080.083-0.034-0.015-0.0700.012-0.0271.0000.0480.0870.0830.0120.0000.0130.1520.0170.0640.1160.1160.1160.1160.0850.0830.0910.0001.000
days_remaining-0.0740.0020.047-0.551-0.1260.197-0.039-0.0370.1430.014-0.129-0.0370.010-0.0050.0040.0650.0330.0650.0450.0481.000-0.2160.0880.0640.0220.0630.0740.0100.0200.0460.0460.0460.0460.0650.0880.1960.0400.509
years_active0.035-0.0020.0850.044-0.043-0.9580.0550.065-0.211-0.0620.2010.0640.016-0.0050.003-0.0300.0210.0460.0780.087-0.2161.0000.0760.1230.0490.1540.1420.0370.0410.0450.0450.0450.0450.0300.0760.1930.0401.000
listing_type_id0.0180.0150.1010.0140.0070.0530.0580.0730.0000.0350.0400.0680.0800.0450.0570.0000.0200.0200.0420.0830.0880.0761.0000.0170.0000.0330.2130.0000.0000.0000.0000.0000.0000.4721.0000.1070.0001.000
shipping__logistic_type0.0570.0070.0150.0760.0340.1400.1560.1560.0640.0430.0610.1570.1580.0930.1450.0920.0920.0490.1000.0120.0640.1230.0171.0001.0000.2080.1290.0420.2980.1840.1840.1840.1840.0500.0170.1200.1120.816
shipping__mode0.0160.0000.0000.0000.0000.0440.0120.0000.0000.0000.0000.0000.0000.0170.0000.0000.0130.0000.0300.0000.0220.0490.0001.0001.0000.0920.0380.0000.0330.0000.0000.0000.0000.0000.0000.0260.0001.000
shipping__store_pick_up0.0190.0000.0000.0120.0000.1430.2790.3130.0230.0000.0300.3250.3240.1490.2860.2550.4460.0100.2050.0130.0630.1540.0330.2080.0921.0000.0190.0000.0600.0240.0240.0240.0240.0500.0330.2150.0001.000
shipping__free_shipping0.0490.0000.1370.0350.0310.0940.1010.1390.0100.0000.0620.1380.1580.1160.1140.0630.0700.0290.0920.1520.0740.1420.2130.1290.0380.0191.0000.0140.0300.0390.0390.0390.0390.0630.2130.2840.0290.000
seller__seller_reputation__level_id0.0140.0070.0300.0000.0000.0660.0000.0000.1900.0350.2290.0000.0000.5470.0000.5170.0000.5250.0000.0170.0100.0370.0000.0420.0000.0000.0141.0001.0000.1760.1760.1760.1760.0000.0000.0320.0001.000
seller__seller_reputation__power_seller_status0.0130.0000.0600.0230.0070.0350.0900.0970.0500.0770.0670.0960.1050.1800.0930.0840.0600.0450.0650.0640.0200.0410.0000.2980.0330.0600.0301.0001.0000.1800.1800.1800.1800.0230.0000.0910.0381.000
seller__seller_reputation__metrics__sales__period0.0390.0000.1360.0090.0000.0420.0490.0530.0670.1850.1720.0530.0580.1140.0510.1110.0310.1350.0340.1160.0460.0450.0000.1840.0000.0240.0390.1760.1801.0000.9990.9990.9990.0000.0000.0530.0000.707
seller__seller_reputation__metrics__claims__period0.0390.0000.1360.0090.0000.0420.0490.0530.0670.1850.1720.0530.0580.1140.0510.1110.0310.1350.0340.1160.0460.0450.0000.1840.0000.0240.0390.1760.1800.9991.0000.9990.9990.0000.0000.0530.0000.707
seller__seller_reputation__metrics__delayed_handling_time__period0.0390.0000.1360.0090.0000.0420.0490.0530.0670.1850.1720.0530.0580.1140.0510.1110.0310.1350.0340.1160.0460.0450.0000.1840.0000.0240.0390.1760.1800.9990.9991.0000.9990.0000.0000.0530.0000.707
seller__seller_reputation__metrics__cancellations__period0.0390.0000.1360.0090.0000.0420.0490.0530.0670.1850.1720.0530.0580.1140.0510.1110.0310.1350.0340.1160.0460.0450.0000.1840.0000.0240.0390.1760.1800.9990.9990.9991.0000.0000.0000.0530.0000.707
installments__quantity0.0080.0110.0700.0000.0000.0450.0750.0690.0000.0480.0320.0700.0780.0420.0400.0000.0230.0050.0350.0850.0650.0300.4720.0500.0000.0500.0630.0000.0230.0000.0000.0000.0001.0000.4720.0570.0001.000
installments__rate0.0180.0150.1000.0140.0070.0530.0580.0730.0000.0350.0400.0680.0800.0450.0570.0000.0200.0200.0420.0830.0880.0761.0000.0170.0000.0330.2130.0000.0000.0000.0000.0000.0000.4721.0000.1070.0001.000
Categoria0.0000.0000.0880.0830.0420.1500.1580.1730.0510.0190.0870.1760.1770.0760.1290.0950.1450.0670.1170.0910.1960.1930.1070.1200.0260.2150.2840.0320.0910.0530.0530.0530.0530.0570.1071.0000.0000.707
seller__car_dealer0.0670.0360.0000.0000.0000.0790.0000.0000.0000.0000.0000.0000.0000.0000.0000.0700.0000.0000.0000.0000.0400.0400.0000.1120.0000.0000.0290.0000.0380.0000.0000.0000.0000.0000.0000.0001.0001.000
seller__car_dealer_logo0.0000.6671.0001.0001.0000.7071.0001.0001.0001.0001.0001.0001.0000.7071.0001.0001.0001.0001.0001.0000.5091.0001.0000.8161.0001.0000.0001.0001.0000.7070.7070.7070.7071.0001.0000.7071.0001.000

Missing values

2023-03-24T00:29:17.489134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-24T00:29:18.316149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-24T00:29:18.823063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

df_indexidtitlethumbnail_idlisting_type_idpermalinkcategory_iddomain_idorder_backendpricesold_quantityavailable_quantitytagsshipping__logistic_typeshipping__modeshipping__store_pick_upshipping__free_shippingshipping__tagsseller__idseller__nicknameseller__tagsseller__seller_reputation__level_idseller__seller_reputation__power_seller_statusseller__seller_reputation__transactions__canceledseller__seller_reputation__transactions__completedseller__seller_reputation__transactions__ratings__negativeseller__seller_reputation__transactions__ratings__neutralseller__seller_reputation__transactions__ratings__positiveseller__seller_reputation__transactions__totalseller__seller_reputation__metrics__sales__periodseller__seller_reputation__metrics__sales__completedseller__seller_reputation__metrics__claims__periodseller__seller_reputation__metrics__claims__rateseller__seller_reputation__metrics__claims__valueseller__seller_reputation__metrics__delayed_handling_time__periodseller__seller_reputation__metrics__delayed_handling_time__rateseller__seller_reputation__metrics__delayed_handling_time__valueseller__seller_reputation__metrics__cancellations__periodseller__seller_reputation__metrics__cancellations__rateseller__seller_reputation__metrics__cancellations__valueinstallments__quantityinstallments__amountinstallments__ratedays_remainingyears_activeCategoriaseller__car_dealerseller__car_dealer_logovariation_filters
00MLA1112140771Pileta Inflable Redonda Bestway Kiddie Lounge 51061 De 61cm X 15cm 21l Azul623284-MLA53605777143_022023gold_prohttps://www.mercadolibre.com.ar/pileta-inflable-redonda-bestway-kiddie-lounge-51061-de-61cm-x-15cm-21l-azul/p/MLA15763166MLA11226MLA-INFLATABLE_POOLS11692.92501['lightning_deal', 'good_quality_picture', 'good_quality_thumbnail', 'immediate_payment', 'cart_eligible', 'best_seller_candidate', 'shipping_guaranteed']fulfillmentme2FalseFalse['fulfillment', 'self_service_out']322650862GLOBAL ROSS['normal', 'eshop', 'mshops', 'credits_profile', 'messages_as_seller']5_greenplatinum510101360.020.020.961064660 days228160 days0.0025660 days0.0026660 days0.000006.0282.150.0068334juguetesNaNNaNNaN
11MLA1275803410Cry Babies Fantasy Dreamy Imc Toys 99180im843969-MLA48269133730_112021gold_specialhttps://www.mercadolibre.com.ar/cry-babies-fantasy-dreamy-imc-toys-99180im/p/MLA15084428MLA2968MLA-DOLLS215990.0200100['good_quality_picture', 'good_quality_thumbnail', 'immediate_payment', 'cart_eligible', 'best_seller_candidate', 'meli_choice_candidate', 'shipping_guaranteed']cross_dockingme2FalseTrue['self_service_in', 'mandatory_free_shipping']355576322MORA PAÑALERA['normal', 'eshop', 'mshops', 'credits_profile', 'messages_as_seller']5_greenplatinum50781188960.040.020.9412397460 days2286260 days0.007517960 days0.019243260 days0.00561356.03953.5348.3572234juguetesNaNNaNNaN
22MLA1109575910Juego De Cartas Desconectados En Palabras633026-MLA48408537360_122021gold_specialhttps://www.mercadolibre.com.ar/juego-de-cartas-desconectados-en-palabras/p/MLA17840062MLA1161MLA-BOARD_GAMES36499.05000250['good_quality_picture', 'good_quality_thumbnail', 'loyalty_discount_eligible', 'immediate_payment', 'cart_eligible', 'best_seller_candidate', 'shipping_guaranteed']fulfillmentme2FalseFalse['fulfillment', 'self_service_in']701499356ENPALABRAS['normal', 'mshops', 'credits_profile', 'messages_as_seller']5_greenplatinum29681720.030.000.97846860 days239560 days0.0000160 days0.0016460 days0.000016.01606.8848.3568152juguetesNaNNaNNaN
33MLA1110677111Juego De Mesa Código Secreto Czech Games Edition Devir960516-MLA44936648183_022021gold_specialhttps://www.mercadolibre.com.ar/juego-de-mesa-codigo-secreto-czech-games-edition-devir/p/MLA7853901MLA1161MLA-BOARD_GAMES49500.02501['good_quality_picture', 'good_quality_thumbnail', 'loyalty_discount_eligible', 'standard_price_by_channel', 'brand_verified', 'immediate_payment', 'cart_eligible', 'shipping_guaranteed']fulfillmentme2FalseTrue['fulfillment', 'self_service_out', 'mandatory_free_shipping']98434282INVICTVSJUEGOS['normal', 'eshop', 'credits_profile', 'mshops', 'messages_as_seller']5_greenplatinum994222180.010.010.982321260 days334760 days0.0008360 days0.0026960 days0.001456.02348.8848.35682216juguetesNaNNaNNaN
44MLA1240728057Bellies Bebe Interactivo Beth Edic Especial Int 15145 Orig994457-MLA49925702800_052022gold_specialhttps://www.mercadolibre.com.ar/bellies-bebe-interactivo-beth-edic-especial-int-15145-orig/p/MLA19765898MLA2968MLA-DOLLS521900.001['good_quality_thumbnail', 'brand_verified', 'good_quality_picture', 'immediate_payment', 'cart_eligible', 'best_seller_candidate', 'shipping_guaranteed']fulfillmentme2FalseTrue['fulfillment', 'self_service_in', 'mandatory_free_shipping']120806491JUG.OSITO AZUL['brand', 'large_seller', 'mshops', 'credits_profile', 'messages_as_seller']5_greenplatinum608119200.030.020.951252860 days187060 days0.0025560 days0.03135860 days0.0142286.05414.7848.35720010juguetesNaNNaNNaN
55MLA1108944811Juego De Mesa Fantasma Blitz Zoch Zum Spielen Devir788441-MLA46818974526_072021gold_specialhttps://www.mercadolibre.com.ar/juego-de-mesa-fantasma-blitz-zoch-zum-spielen-devir/p/MLA7883593MLA1161MLA-BOARD_GAMES66899.91001['brand_verified', 'good_quality_picture', 'good_quality_thumbnail', 'immediate_payment', 'cart_eligible', 'shipping_guaranteed']fulfillmentme2FalseFalse['fulfillment', 'MLA-chg-threshold-Feb-23', 'self_service_in']200611461PHOTOPRINT_STORE['normal', 'credits_profile', 'mshops', 'messages_as_seller']5_greenplatinum37863920.050.030.92677060 days152760 days0.01051760 days0.02123260 days0.0080136.01706.0048.3568107juguetesNaNNaNNaN
66MLA1164168440Juego De Cartas A.t.r. Ahora Todos Reímos Buró719737-MLA51168724015_082022gold_specialhttps://www.mercadolibre.com.ar/juego-de-cartas-atr-ahora-todos-reimos-buro/p/MLA19538917MLA1161MLA-BOARD_GAMES78091.02501['good_quality_picture', 'good_quality_thumbnail', 'immediate_payment', 'cart_eligible', 'best_seller_candidate', 'shipping_guaranteed']cross_dockingme2FalseTrue['MLA-chg-threshold-ago-22', 'self_service_in', 'mandatory_free_shipping']148114094MUNDO MANIAS['normal', 'credits_priority_4', 'credits_profile', 'eshop', 'mshops', 'medium_seller_advanced', 'messages_as_seller']5_greenplatinum2261645790.010.010.986684060 days1425160 days0.00081360 days0.00152260 days0.000466.02000.5048.3571329juguetesNaNNaNNaN
77MLA924307817Ajedrez Profesional Staunton Plastigal 160712088-MLA44676313815_012021gold_specialhttps://www.mercadolibre.com.ar/ajedrez-profesional-staunton-plastigal-160/p/MLA16102093MLA412670MLA-BOARD_GAMES89199.95001['brand_verified', 'good_quality_picture', 'good_quality_thumbnail', 'immediate_payment', 'cart_eligible', 'best_seller_candidate', 'shipping_guaranteed']fulfillmentme2FalseTrue['fulfillment', 'self_service_in', 'mandatory_free_shipping']45691061STORELELABSKYLIGHT['normal', 'mshops', 'credits_profile', 'messages_as_seller']5_greenplatinum3375620250.020.020.966540060 days970060 days0.00919460 days0.00959160 days0.0089926.02274.6848.35667518juguetesNaNNaNNaN
88MLA923773432Juego De Mesa Rummy & Burako Clásico Ruibal 1056946916-MLA51774945309_092022gold_specialhttps://www.mercadolibre.com.ar/juego-de-mesa-rummy-burako-clasico-ruibal-1056/p/MLA6829078MLA1161MLA-BOARD_GAMES95800.05001['brand_verified', 'good_quality_picture', 'good_quality_thumbnail', 'immediate_payment', 'cart_eligible', 'best_seller_candidate', 'shipping_guaranteed']cross_dockingme2FalseFalse['MLA-chg-threshold-ago-22', 'self_service_in']45689688TECNOPARAVOS['normal', 'credits_priority_4', 'credits_profile', 'eshop', 'mshops', 'messages_as_seller']5_greenplatinum631209520.000.001.002158360 days371660 days0.00311260 days0.0014560 days0.000016.01434.0548.35719512juguetesNaNNaNNaN
99MLA1133264997El Duende Azul Tomy Bebé En Cochecito728513-MLA49643829193_042022gold_specialhttps://www.mercadolibre.com.ar/el-duende-azul-tomy-bebe-en-cochecito/p/MLA13864214MLA2968MLA-DOLLS104490.02001['brand_verified', 'good_quality_picture', 'good_quality_thumbnail', 'moderation_penalty', 'immediate_payment', 'cart_eligible', 'best_seller_candidate', 'shipping_guaranteed']fulfillmentme2FalseFalse['fulfillment', 'MLA-chg-threshold-ago-22', 'self_service_in']75694306JUGUETERIA CEBRA['brand', 'large_seller', 'eshop', 'mshops', 'credits_profile', 'messages_as_seller']5_greenplatinum58291268350.050.030.9213266460 days2262660 days0.004510960 days0.013831460 days0.0029696.01110.1548.35699313juguetesNaNNaNNaN
df_indexidtitlethumbnail_idlisting_type_idpermalinkcategory_iddomain_idorder_backendpricesold_quantityavailable_quantitytagsshipping__logistic_typeshipping__modeshipping__store_pick_upshipping__free_shippingshipping__tagsseller__idseller__nicknameseller__tagsseller__seller_reputation__level_idseller__seller_reputation__power_seller_statusseller__seller_reputation__transactions__canceledseller__seller_reputation__transactions__completedseller__seller_reputation__transactions__ratings__negativeseller__seller_reputation__transactions__ratings__neutralseller__seller_reputation__transactions__ratings__positiveseller__seller_reputation__transactions__totalseller__seller_reputation__metrics__sales__periodseller__seller_reputation__metrics__sales__completedseller__seller_reputation__metrics__claims__periodseller__seller_reputation__metrics__claims__rateseller__seller_reputation__metrics__claims__valueseller__seller_reputation__metrics__delayed_handling_time__periodseller__seller_reputation__metrics__delayed_handling_time__rateseller__seller_reputation__metrics__delayed_handling_time__valueseller__seller_reputation__metrics__cancellations__periodseller__seller_reputation__metrics__cancellations__rateseller__seller_reputation__metrics__cancellations__valueinstallments__quantityinstallments__amountinstallments__ratedays_remainingyears_activeCategoriaseller__car_dealerseller__car_dealer_logovariation_filters
199813987MLA859686032Martillo Demoledor Total 20 Joules Hexagonal 1300w 2 Cincele963608-MLA42050668479_062020gold_specialhttps://articulo.mercadolibre.com.ar/MLA-859686032-martillo-demoledor-total-20-joules-hexagonal-1300w-2-cincele-_JMMLA372066MLA-ELECTRIC_DEMOLITION_HAMMERS4149183.875001['brand_verified', 'extended_warranty_eligible', 'good_quality_picture', 'good_quality_thumbnail', 'immediate_payment', 'cart_eligible', 'shipping_guaranteed']fulfillmentme2FalseTrue['fulfillment', 'self_service_in', 'mandatory_free_shipping']265484143PIDEWEB['normal', 'credits_profile', 'mshops', 'messages_as_seller']5_greenplatinum115432607260.040.030.9327226960 days4155160 days0.009140060 days0.006727060 days0.0009416.012160.7148.3568105herramientasFalseNaNNaN
199823988MLA901131431Regla Trapezoidal De Albañil -revoque/yeso 1,5 Metros Cuotas854181-MLA44134004469_112020gold_prohttps://articulo.mercadolibre.com.ar/MLA-901131431-regla-trapezoidal-de-albanil-revoqueyeso-15-metros-cuotas-_JMMLA429026MLA-CONSTRUCTION_FINISHING_TROWELS425821.201001['brand_verified', 'good_quality_picture', 'good_quality_thumbnail', 'loyalty_discount_eligible', 'immediate_payment', 'cart_eligible']xd_drop_offme2FalseFalse['MLA-chg-threshold-ago-22', 'self_service_in']265859712PERFILESDEALUMINIO.NET['normal', 'eshop', 'mshops', 'credits_profile', 'messages_as_seller']5_greenplatinum21534570.020.010.97367260 days41060 days0.0000060 days0.0000060 days0.000006.0970.200.0065015herramientasFalseNaNNaN
199833989MLA1220123578Hidrolavadora Eléctrica Philco Mjphi117 1400w + Accesorios832507-MLA52223742789_102022gold_prohttps://articulo.mercadolibre.com.ar/MLA-1220123578-hidrolavadora-electrica-philco-mjphi117-1400w-accesorios-_JMMLA30840MLA-ELECTRIC_PRESSURE_WASHERS4328299.00501['good_quality_picture', 'good_quality_thumbnail', 'extended_warranty_eligible', 'immediate_payment', 'cart_eligible', 'shipping_guaranteed']fulfillmentme2FalseTrue['fulfillment', 'self_service_out', 'mandatory_free_shipping']39144101BIDCOM['normal', 'eshop', 'developer', 'mshops', 'credits_profile', 'messages_as_seller']5_greenplatinum201642504560.060.030.9127062060 days3925860 days0.006427260 days0.004417560 days0.00632696.04716.500.00718615herramientasFalseNaNNaN
199843990MLA877949190Embrague Metálico Para Desmalezadoras Chinas De 43 A 52cc783024-MLA50862965663_072022gold_specialhttps://articulo.mercadolibre.com.ar/MLA-877949190-embrague-metalico-para-desmalezadoras-chinas-de-43-a-52cc-_JMMLA413604MLA-TOOL_ACCESSORIES_AND_SPARES442067.002501['good_quality_picture', 'good_quality_thumbnail', 'loyalty_discount_eligible', 'immediate_payment', 'cart_eligible', 'shipping_guaranteed']cross_dockingme2FalseFalse['self_service_in']268911045LOMASBULLSERVICE['normal', 'eshop', 'mshops', 'credits_profile', 'messages_as_seller']5_greenplatinum1187292450.010.010.983043260 days608860 days0.00533460 days0.0013860 days0.000646.0511.0748.3564055herramientasFalseNaNNaN
199853991MLA1263261654Pico Standard Grasera Engrasadoras Agarre 4 Uñas712154-MLA52638779508_112022gold_specialhttps://articulo.mercadolibre.com.ar/MLA-1263261654-pico-standard-grasera-engrasadoras-agarre-4-unas-_JMMLA371996MLA-GREASE_GUNS451279.00501['standard_price_by_channel', 'good_quality_picture', 'good_quality_thumbnail', 'immediate_payment', 'cart_eligible']cross_dockingme2FalseFalse['self_service_in']63869733KIT HERRAMIENTAS['normal', 'credits_active_borrower', 'credits_profile', 'eshop', 'mshops', 'messages_as_seller']5_greenplatinum481117050.010.010.981218660 days246460 days0.0019560 days0.0004160 days0.0054146.0316.2348.35721511herramientasFalseNaNNaN
199863992MLA806671534Hidrolavadora Philco 105 Bar 1400w Con Espumero939546-MLA45226866923_032021gold_prohttps://articulo.mercadolibre.com.ar/MLA-806671534-hidrolavadora-philco-105-bar-1400w-con-espumero-_JMMLA30840MLA-ELECTRIC_PRESSURE_WASHERS4624363.00250250['brand_verified', 'extended_warranty_eligible', 'good_quality_picture', 'good_quality_thumbnail', 'incomplete_technical_specs', 'immediate_payment', 'cart_eligible', 'shipping_guaranteed']cross_dockingme2FalseTrue['self_service_in', 'mandatory_free_shipping']184198511TIENDASUR.['normal', 'eshop', 'mshops', 'credits_profile', 'messages_as_seller']5_greenplatinum64271284630.020.020.9613489060 days2399060 days0.005413860 days0.0936202060 days0.0022586.04060.500.0060055herramientasFalseNaNNaN
199873993MLA1123490998Bordeadora Nober 212 Max Color Naranja 220v661228-MLA46192795432_052021gold_prohttps://www.mercadolibre.com.ar/bordeadora-nober-212-max-color-naranja-220v/p/MLA9026334MLA411878MLA-ELECTRIC_BRUSH_CUTTERS_AND_STRING_TRIMMERS4736814.0051['brand_verified', 'extended_warranty_eligible', 'good_quality_picture', 'good_quality_thumbnail', 'immediate_payment', 'cart_eligible']xd_drop_offme2FalseTrue['self_service_in', 'mandatory_free_shipping']253798104DINFER FERRETERIA['normal', 'credits_open_market', 'eshop', 'credits_profile', 'mshops', 'messages_as_seller']5_greenplatinum14341650.010.010.98430860 days53160 days0.0072460 days0.0154860 days0.000006.06135.670.0069275herramientasFalseNaNNaN
199883994MLA908766072Minitorno Dremel 4000 220v 175 W Eléctrico + 26 Accesorios705010-MLA44922506957_022021gold_specialhttps://articulo.mercadolibre.com.ar/MLA-908766072-minitorno-dremel-4000-220v-175-w-electrico-26-accesorios-_JMMLA10072MLA-MICRO_ROTARY_TOOLS4831830.005001['deal_of_the_day', 'extended_warranty_eligible', 'good_quality_picture', 'good_quality_thumbnail', 'immediate_payment', 'cart_eligible', 'shipping_guaranteed']fulfillmentme2FalseTrue['fulfillment', 'mandatory_free_shipping']608846165MERCADOLIBRE ELECTRONICA_AR['brand', 'large_seller', 'credits_profile', 'messages_as_seller']5_greenplatinum474307594300.090.020.8980686060 days10184360 days0.0098106260 days0.0000060 days0.000006.07869.9748.3572792herramientasFalseNaNNaN
199893995MLA766557238Pinza Alicate Multiherramientas Stanley 16 Funciones 92841860969-MLA40009684434_122019gold_specialhttps://articulo.mercadolibre.com.ar/MLA-766557238-pinza-alicate-multiherramientas-stanley-16-funciones-92841-_JMMLA412520MLA-TOOL_AND_CONSTRUCTION_SUPPLIES493440.00500500['brand_verified', 'good_quality_picture', 'good_quality_thumbnail', 'loyalty_discount_eligible', 'immediate_payment', 'cart_eligible', 'shipping_guaranteed']cross_dockingme2FalseFalse['self_service_in']184198511TIENDASUR.['normal', 'eshop', 'mshops', 'credits_profile', 'messages_as_seller']5_greenplatinum64271284630.020.020.9613489060 days2399060 days0.005413860 days0.0936202060 days0.0022586.0850.5448.3569705herramientasFalseNaNNaN
199903996MLA1130045765Disco Laminas De Fieltro 115mm Pulido Espejo Harden631661-MLA50022908155_052022gold_specialhttps://articulo.mercadolibre.com.ar/MLA-1130045765-disco-laminas-de-fieltro-115mm-pulido-espejo-harden-_JMMLA372119MLA-CUTTING_AND_GRINDING_DISCS_AND_STONES501029.6515050['good_quality_picture', 'good_quality_thumbnail', 'immediate_payment', 'cart_eligible', 'shipping_guaranteed']cross_dockingme2FalseFalse['self_service_in']243229286FERRETERIA FERNOLUZ['normal', 'credits_priority_2', 'credits_profile', 'eshop', 'mshops', 'messages_as_seller']5_greenplatinum49581231820.030.030.9412814060 days1688060 days0.00529360 days0.012320460 days0.0017306.0254.5848.3569716herramientasFalseNaNNaN